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The self-validating sensor: rationale, definitions and examples


Abstract and Figures

Traditionally, the industrial sensor has been viewed as a simple signal generator. The application of microprocessor technology, digital communications and fault detection techniques, coupled with increasing demands for measurement quality assurance, have rendered inadequate such a simplistic view. In this paper a new sensor model is proposed which encompasses new demands and capabilities. This self-validating sensor performs self-diagnostics and generates a variety of data types, including the on-line uncertainty of each measurement. A demonstration system is described, based upon a coriolis mass flow meter.
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Although the sensor provides the on-line data which is
a pre-requisite to monitoring and controlling any tech-
nical process, it is fair to say that in the past it has been
one of the more neglected elements of the process
plant. Far more attention has been paid to, say, the
theory and implementation of feedback control, and it
is still frequently assumed that the control system
should be able to compensate for measurement limita-
tions. Under-instrumentation remains a common prac-
tice in the process industries.
As until recently the only continuous contact between
the sensor and the control system has been the unidi-
rectional flow of measurement data (usually based
upon the 4-20mA standard), it is understandable that
the sensor has been viewed as a simple signal gener-
ator, and that its data has been assumed to be ‘correct’
unless over-ridden by extraordinary action (e.g. by the
operator). This single stream of information has been
used in a variety of ways: for monitoring the process,
for feedback control, and also for ensuring safety
through the use of hardwired trips.
As far as the control system is concerned, it is irrelevant
whether the data has been generated by a simple ther-
mocouple or an on-line analyser, and in this paper the
term ‘sensor’ will be used to describe any on-line
system generating measurement information.
More recently the sensor has been receiving greater
attention, due in part to the greater demands placed on
all aspects of process operation:
Competition has resulted in higher goals for prod-
uct quality, and plant efficiency and availability.
The crucial role of measurement in achieving
these aims is becoming more widely acknow-
Safety standards are constantly rising. Measure-
ments are the primary means of identifying poten-
tially hazardous circumstances. In addition, envi-
ronmental protection legislation is being enacted
and enforced in many countries (for example Bigg
(1990) describes the impact of the Environmental
Protection Bill in the U.K.). Breaking environ-
mental constraints can lead to heavy fines or loss
of license to operate, and sensor data provides
both the first sign of warning to the operator and
demonstration of compliance to the authorities. In
enhancing either safety or environmental protec-
tion aspects of plant operation, it is not sufficient
merely to add more sensors: measurement quality
must be ensured.
There is increasing recognition that, if measure-
ment data is to be used on-line, particularly for
feedback control, then safety cannot be ensured
without some form of on-line fault detection.
Matching these demands are improvements in technol-
ogy which offer enhanced functionality:
New sensor technologies are becoming available,
extending both the set of properties that can be
measured and the environments in which they can
be sampled. Developments range from micro-sen-
sors to ever more sophisticated on-line process
Department of Engineering Science, Parks Road, Oxford OX1 3PJ
Abstract. Traditionally, the industrial sensor has been viewed as a simple signal generator. The application of
microprocessor technology, digital communications and fault detection techniques, coupled with increasing demands for
measurement quality assurance, have rendered inadequate such a simplistic view. In this paper a new sensor model is
proposed which encompasses new demands and capabilities. This self-validating sensor performs self-diagnostics and
generates a variety of data types, including the on-line uncertainty of each measurement. A demonstration system is
described, based upon a coriolis mass flow meter.
Key Words. Self-Validating Sensors; Uncertainty; Fault Detection and Diagnosis; Fieldbus; Coriolis Meter.
Microprocessor-based instrumentation and digi-
tal communications in the field are having a pro-
found effect on the capabilities of the sensor.
The techniques of fault detection are maturing and
are slowly finding more widespread application.
In this paper it is argued that the extended role and
capabilities of the sensor can no longer be described
adequately by a signal generator model. A new concep-
tual model is required which encompasses the extra
capab ilities an d accommodates the additional d emands
placed upon the sensor.
There are three cornerstones to the model of the self-
validating or SEVA sensor (Henry and Clarke 1991,
Henry and Wood 1992) which is described in this
paper: the use of fault detection techniques, the appli-
cation of digital technology, and the use of uncertainty
analysis provided by the science of metrology. Their
interaction is shown in Fig. 1.
Metrology provides a means of analysing all factors
affecting accuracy and calculating a single uncertainty
value as a quality index for each measurement. When
a sensor has an in-built microprocessor, such calcula-
tions can take place on-line. Similarly, fault detection
techniques can be applied within the device to provide
internal diagnostics. Finally, by applying uncertainty
analysis to a faulty sensor, it is possible to assess the
impact of a fault upon measurement quality, thereby
retaining the availability of the measurement despite
the presence of a sensor fault.
Each of the three key areas are ex amined i n more de tail
in subsequent sections. There follows an analysis of
why a new sensor model is required, and then a set of
definitions describing the SEVA sensor. The final sec-
tion describes a system which demonstrates the SEVA
concepts, based upon a Coriolis mass flow meter.
The first attempts to widen the naive signal generator
model of the sensor have been made in the fault detec-
tion domain. This work was stimulated in part by the
evident fact that acting on data from faulty sensors can
lead to disaster. The resulting model of the sensor can
be described as follows:
A sensor is designed to generate measurement data (i.e.
to transmit an estimate of a process parameter). In
practice the measurement is not a perfect repre-
sentation of the process parameter: the effects of the
sensor (including faults) and other process parameters
or plant components (including those attributable to
‘‘faulty’’ behaviour) are also present in the measure-
ment signal (see Fig. 2).
With the limitations of analogue communication,
measurement data has been the only source of informa-
tion about the process and sensor. Ingeniously, work-
ers in the fault detection domain have attempted to
extract sensor, plant and process fault information from
measurement data, but the inherent difficulties of dis-
tinguishing the separate strands from within a single
analogue signal are universally acknowledged.
For example, Fig. 3 shows the output of a mass flow
meter placed on an experimental rig. The rise in value
which begins at t = 10s is due to the onset of a sensor
fault, while the true mass flow rate remains approxi-
Metrology Digital
Fault Detection
Fig 1. Cornerstones of SEVA Model
Estimate of Process Variable
Distortions due to Sensor
Distortions due to other
Process Parameters
4 - 20 mA
Fig. 2. Extended Model of the Analogue Sensor
Reported Flow Rate
Actual Flow Rate
0 5 10 15 20 25 30 35 40 45
2.40 2.45 2.50 2.55 2.60 2.65
Time (s)
Mass Flow (kg/s)
Fig. 3. Step change in measurement due to sensor fault
mately constant at about 2.5 kg/s. However, in the
absence of other information, it might equally be in-
ferred from the reported flowrate that the sensor is
functioning properly and that the process mass flow
has in fact risen. It is the task of fault detection to
distinguish between plant or sensor faults and legiti-
mate changes in a measurement signal.
This task is made more difficult by the impact of
feedback control in the presence of a fault, as described
in the next subsection.
2.1. Feedback Control
Feedback control adds to the complexity of fault detec-
tion in process plant by masking measurement devia-
tions that might indicate a fault, and by making it
difficult to distinguish between sensor, actuator and
plant failure. For example, Fig. 4 shows the results of
an experiment performed on a test rig using a mass flow
meter and a valve. A PID controller is used to maintain
a constant flow rate. Initially there are regular devia-
tions about the set point of 1.0 due to slight stiction in
the valve. At t = 30s there is a disturbance, to which the
controller responds, and after t = 60s the reported mass
flow rate resumes its characteristic behaviour. In fact
the disturbance at t = 30s is due to a sensor fault of the
same type as is shown in Fig. 3. However, without the
benefit of knowing the nature of the fault and the actual
flow rate through the rig, it is possible to construct a
number of hypotheses to explain the given pattern of
measurement disturbance and control response, as fol-
Process disturbance. A variation in the process (such as
a rise in fluid density or pressure) increases the mass flow
rate, and the controller responds by reducing the flow rate
back towards the set point.
Valve fault. A fault in the valve (e.g. a deviation in the
supplied air pressure) results in the valve sliding further
open (without a corresponding increase in the control
signal) thus causing a higher flow rate. The controller acts
to correct the mass flow. A disparity has been introduced
between the control signal and valve position.
Sensor fault. A fault in the sensor introduces a positive
bias into the measurement. The controller responds to the
apparent increase in the flow rate, thereby causing the true
mass flow to be maintained at a level below the set point.
As stated above, this is what actually happened.
Comparing the reported and actual flow rates in graph 3a,
a 6% discrepancy has been introduced.
Several conclusions can be drawn from this example:
Feedback control makes it difficult to distinguish
between sensor, actuator and process faults.
The effects of sensor faults are particularly seri-
ous, as feedback acts to suppress any measure-
ment deviation. Thus after any initial disturbance
the measurement may appear deceptively normal.
There may be a limited time window for observ-
ing the gross effects of a sensor, actuator or proc-
ess fault.
There may be a ‘knock on’ effect. As the true flow
rate stabilises after t = 50s at 6% lower than the
set point, further problems may occur down-
stream of the valve. Similarly, had the process
disturbance hypothesis been correct, the rise in
flow rate may have been caused by a fault occur-
ring elsewhere in the plant upstream of the valve
and sensor. In either case, after the initial distur-
bance the reported flow rate returns to the set
point, making process ‘trouble-shooting’ diffi-
The consequences of a fault depend very much upon
the particular application. At worst it could be disas-
trous, at best it will be detrimental to product quality,
plant efficiency, or both. A vigilant operator may no-
tice certain faults, particularly those affecting open
loop measurements, such as in Fig. 3, where there may
be a permane nt dis crepancy between the r eported v alue
and its usual level. However, faults occurring in closed
Reported Flow Rate
Actual Flow Rate
0.0 0.5 1.0 1.5
Time (s)
Mass Flow (kg/s)
0 10 20 30 40
Time (s)
Control Output (%)
Fig. 4. Sensor fault and feedback response: (a) mass flow
(b) valve position
loops such as in Fig. 4 are far more difficult to notice,
particularly as the operator is likely to be monitoring
measurement data alone. Furthermore, the ‘knock on’
effect may have the result that when symptoms are
observed, they may be at some distance from the source
of the problem.
To achieve the goals of improved efficiency and safety,
it is therefore highly desirable to have an automated
system monitoring plant behaviour for faults.
2.2. Fault Detection, Identification and Accommoda-
Given the difficulties of detecting faults in individual
plant components, the most common strategy for de-
veloping an automated fault detection system is to pool
data from several sensors, actuators and/or controllers.
This approach is usually termed Fault Detection, Iden-
tification and Accommodation (FDIA) (Frank, 1990),
as there are several stages involved in the procedure.
Firstly some model of the process is created. A soft-
ware system is then implemented which monitors for
inconsistencies between the model and on-line data
(fault detection). There follows the task of identifying
which plant component (whether sensor, actuator or
other item) is responsible for the inconsistency. Fi-
nally, the system must respond to the presence of the
fault in a safe and efficient manner (accommodation),
although this is such a plant- and device-specific activ-
ity that it is scarcely more than mentioned in the
literature, and in practice most papers are restricted to
FDI. There are two principal approaches of the FDI
Analytical Redundancy (AR). This approach exploits
the implicit redundancy in the static and dynamic
relationships between measurements and actuator inputs,
using a mathematical model (Fig. 5).
A vector of functions, called the residual, is generated,
which is examined by the decision-making process,
resulting in a fault decision. Each component of the
residual has the property that (ideally) it is zero as long
as plant operation remains normal, but some or all of
these components become non-zero when an inconsis-
tency occurs. Thus the residual components verify the
continuing redundancy relationship between the meas-
urement outputs and actuator inputs.
Fault detection entails no more than monitoring the
magnitude of the residual vector. The identification of
the source of the fault is more difficult, and a number
of strategies have been devised for the design of the
residual, each reflecting a different approach to fault
identification. In the parity space approach (Patton and
Chen, 1991), for example, the space formed by possi-
ble residual values is spanned by a set of fault signature
directions. The residual is designed to move in a par-
ticular direction when the corresponding fault occurs.
Detailed descriptions of the various strategies, imple-
mentation issues and the latest developments in ana-
lytical redundancy can be found in the survey papers
of Frank (1991), Gertler (1991), Patton and Chen
(1991) and Isermann (1991).
Knowledge-based Methods (KBM). In this approach,
qualitative models of the process are built and
manipulated using heuristic reasoning. Such models
might include knowledge concerning operational
conditions and associated fault modes, patterns of signal
behaviour characteristic of particular faults, or historical
fault statistics. Reflecting a less rigorous mathematical
analysis of the plant, there is a considerable variety of
techniques that can be employed. These include expert
systems (Tzafestas, 1989 and 1991), neural nets (Hoskins
et al., 1988; Himmelblau, 1992), petri nets (Maßberg and
Seifert, 1991) and fuzzy logic (Vachkov and Matsuyama,
Measurement Aberration Detection (MAD). A third
approach is to examine the output of the single sensor for
indications of faults. The techniques are described by
Yung and Clarke (1989) although in their paper they use
the term ‘sensor validation’. When applying MAD it is
assumed that the ‘true’ measurement signal has certain
time and/or frequency domain properties and that ‘faults’
(defined as deviations from normal behaviour) may occur
randomly at any time. For example, Fig. 6 shows the
frequency spectrum of a sensor output, in which the lower
frequencies are considered to be ‘measurement’ and the
high frequencies ‘sensor’ related. The presence of a high
frequency harmonic may be interpreted as implying a
sensor fault. Faults are classified in terms of how they
change the behaviour of the signal (e.g. bias, noisy,
saturation), rather than suggesting any underlying,
sensor-specific cause for the fault (e.g. corroded
MAD techniques have also been used to detect plant
faults. As shown in Fig. 2, the analogue measurement
carries a component that is ‘‘process related’’, i.e. it is
Fault Decision
Actuators Sensors
Faults Faults
Residual vector
Fig. 5. FDI scheme using Analytical Redundancy (Patton and
Chen 1991)
affected by aspects of the process (or plant) other than
the measurand (or sensor). In practice such data has
been used by operators who, by observing minor fluc-
tuations or characteristic patterns in a measurement
signal, are able to gauge the ‘health’ of plant compo-
nents or the process near to the transducer. In effect the
sensor is providing an auxiliary measurement related
to health. Of course such practice is far from systematic
or widespread, depending upon the good fortune of
useful features being recognizable within the measure-
ment signal. However, MAD-type schemes have been
developed to automate the detection of plant-related
phenomena. For example, in lab trials, worn or broken
impellor blades on a nearby pump have been detected
using measurement data from an orifice-plate flow
meter (KB MUSICA 1992). Such work has strong
parallels with acoustic monitoring, which can be used
to provided health data on plant and/or process (Bel-
chamber and Collins 1990). However, in the latter case
signals are observed for their health content only, and
a measurement signal is not used as a ‘carrier’.
2.3. Implementation Issues
When selecting one or more techniques to use in a
particular application, a number of criteria must be
borne in mind, such as the consequences of an undiag-
nosed fault, and available expertise and resources.
However, it is the degree of detailed knowledge of the
plant which is the major determinant of which tech-
niques can be applied.
Analytical Redundancy (AR) is theoretically the most
sensitive method of fault detection, but its effective-
ness is critically dependent upon the availability and
quality of a mathematical model of the process. In
mechanical systems such as aircraft, AR has been
successfully applied, but in chemical plant where only
poor or imprecise models are available, AR remains
difficult. The creation of AR schemes which are robust
to plant disturbances and modelling errors is a major
research topic (Patton et al., 1992). In addition, Frank
(1990) points out that the quality of fault isolation is
heavily dependent upon the number of measurements
that are available in the plant. Even when AR is appli-
cable, Frank (1990) suggests that AR and KBM are
complementary and can (and should) be combined in
a single software package.
As there are a variety of techniques described under the
general heading of Knowledge Based Methods, the key
task is to match available knowledge to the appropriate
technique. Himmelblau (1992) outlines the strengths
and weaknesses of various methods. Lacking the rig-
our of AR it is possible to express far more knowledge
of the process than can be described in a mathematical
model; conversely, there is no guarantee that the rea-
soning employed will be comprehensive or fault-free.
There is also the problem of capturing and encoding
‘process knowledge’ using the selected paradigm(s).
Tzafestas (1991) describes some of the difficulties
encountered in applying ‘first generation’ expert sys-
tems to process fault diagnosis, and points to improve-
ments likely to feature in future systems.
MAD has advantages over a pure FDI scheme in that
there is no need to ‘identify’ which of a number of
sensors has malfunctioned - its own monitor will flag
the guilty party. In addition MAD can be applied
selectively to sensitive or valuable measurements, and
so can be much more cost effective. However, in
practice, detection usually requires a considerable
amount of process- and device-specific tuning, and
there remains the problem of legitimate changes in the
statistical properties of device or process. In addition,
as there is no attempt to correlate data from multiple
sensors, MAD cannot detect faults which are manifest
as inconsistencies between well-behaved signals. Thus
in comprehensive implementations MAD can be used
as a form of diagnostic prefiltering (spotting gross
deviations in individual sensors) to an FDI scheme (see
Fig. 7). Frank (1990), Patton et al (1989) and Isermann
(1991) discuss in more detail the architecture of fault
detection schemes which employ a combination of
Irrespective of the techniques applied, for any FDI
scheme it is in general difficu lt to develop a model that
0 50 100 150 200 250 300 350 400 450 500
sensor fault
Fig. 6. MAD scheme using frequency spectrum
Measurement Fault
Fault Detection and Identification System
Fig. 7. MAD combined with FDI techniques
is robust to process disturbances and modifications to
the plant, and to test thoroughly the fault detection
scheme when it is rarely feasible to induce actual faults
into the plant for ‘practice’.
There are also economic difficulties in transferring FDI
techniques to real applications. As sensor data is
pooled to detect inconsistencies, the smallest viable
application must use data from a number of sensors,
and therefore a model of some unique piece of plant is
normally required. A large investment of modelling
expertise is needed for each plant, as there is little
carry-over of effort between applications, and key per-
sonnel must be available to maintain and update the
system as the plant changes. Potential benefits must be
weighed against this investment, losses in production
while the system is installed, and the perceived risk that
the system may not be successful. When the total
investment is large, it is difficult to sanction such a
project, but without previous successful examples it is
hard to quantify potential benefits. It is not easy when
dealing with cost-conscious management to argue for
funding a project on the grounds that theoretically it
can prevent a major incident once every ten years.
2.4. Integrating Fault Detection and Plant Operation
Given these difficulties, it is perhaps not surprising that
despite the desirability of fault detection, and the ma-
turity of its techniques, it has yet to find widespread
application within the process industries (Halme and
Selkainaho 1991). A reasonable conclusion might be
that either the costs must be lowered, or the perceived
benefits increased, or both.
Fault detection is expensive because:
hundreds of hours of expert labour are required
for each application;
a software system must be constructed in parallel
with, and interfacing to, the control system.
The perceived benefits are insufficient because:
Fault detection is seen as preventative rather than
as enhancing production;
The performance of a fault detection system is in
general difficult to guarantee.
Many of these problems are due to the fact that fault
detection is viewed, and described by its practitioners,
as an activity in isolation. This is particularly true of
AR which is described in purely mathematical terms.
It is up to the implementor to map the reality of the
plant into a mathematical form, code the diagnostic
procedures and map the results of the on-line signifi-
cance tests back into the plant context. AR offers at best
an indication of some inconsistency stemming from a
particular plant component. Any further reasoning
about the true nature of the fault, its potential conse-
quences, and action that must be taken, cannot be
described or manipulated within the rigorous mathe-
matical framework of AR. Of course there is little
practical benefit in performing fault detection unless
these additional aspects are addressed.
If the extra tasks of fault interpretation and accommo-
dation are to be automated, then knowledged-based
techniques are better suited to the task, providing an-
other reason for adopting a mixed architecture in an
FDI system. In any case, much of the effort of creating
an on-line FDI system will be taken up not so much
with fault detection as such, but with knowledge acqui-
sition, interfacing to data acquisition and control sys-
tems, creating a user interface, real-time graphics, an
advisory system, testing, and so on.
One reason for the use of expert system shells in
diagnostic systems is precisely because many of these
facilities are provided within the package, thus reduc-
ing the need for recoding between applications. More
importantly, however, within such shells fault detec-
tion can be combined with other tasks such as produc-
tion scheduling, which can be more easily shown to be
making a positive impact on the balance sheet (Rowan
1992). Typically, payback is demonstrated by im-
proved first pass yield and waste reduction. Similarly,
because of the broader capabilities of such systems it
is economically viable to keep in-house expertise of a
particular shell.
Thus by seeing fault detection as just one facet of the
more general goal of enhancing safety and efficiency,
the add-on costs are reduced and the potential benefits
are seen in terms of e xtra productivity rather than mere
prevention. Looking to the future, Milne (1991) argues
that integration should be taken to the point of largely
eliminating the use of ‘experts’ to encode diagnostic
knowledge. Rather, diagnostic information should be
drawn directly from existing CAD and process data
describing the state of the plant and its organisation:
thus the plant is designed to be diagnosed, and is
diagnosed from its design.
2.5. The SEVA Approach
In the SEVA approach, the reliability and ease of
integration of fault detection is increased, while the
costs are reduced, by ‘factorising out’ the need for
sensor or actuator validation at the system level. In-
stead, these tasks are performed within the devices
themselves, as extra functionality provided by the
manufacturer: sensors are designed to be self-diagnos-
ing. Within each sensor, fault detection is combined
with measurement validation, and fault information is
generated in a standard format which can be readily
used by the control system. This approach is made
possible by the application of digital technology as
described in the next section.
There are two principal areas in which digital technol-
ogy is having a profound impact on process instrumen-
tation. These are the use of microprocessors within
sensors and actuators, and the development of digital
3.1. Digital sensors
The recent development of cheap and powerful micro-
processors has lead to a new generation of digital
sensors and actuators with considerable in-built proc-
essing power. The advantages conferred on instrumen-
tation by the introduction of microprocessors include
the following (de Sa 1988):
Improved accuracy - typically doubled;
Corrections for sensor non-linearity and tempera-
ture effects;
Output in engineering units;
Configurability (e.g. output units, ranging);
Internal storage of location and servicing details;
Internal diagnostics.
For example, Table 1 compares the characteristics of
typical analogue and digital differential pressure sen-
sors: Table 1 Characteristics of Analogue
and Intelligent D.P. Sensors
(% is of full span)
Characteristic Analogue Digital
Accuracy ± 0.25% ± 0.1%
Repeatability ± 0.05% ± 0.02%
Hysteresis ± 0.1% ± 0.02%
Drift (6 months) ± 0.15% ± 0.1%
Ambient Temperature
Effect ± 0.75% ± 0.1%
Static Pressure Effect ± 1.0% ± 0.1%
Blickley (1991) describes not only similar develop-
ments with actuators, but also some of the environ-
mental legislation which has required manufacturers to
use digital technology to enhance and validate the
performance of their devices.
3.2. The Coriolis Mass Flow Meter
Several of the examples described in this paper are
based upon work performed using a commercial Cori-
olis mass flow meter, which is a typical ‘intelligent’
instrument. This microprocessor-based sensor is used
in the petrochemical, chemical and food industries. It
measures three process parameters: mass flow, density
and temperature, and can additionally calculate volu-
metric and total flow. The principle of Coriolis accel-
eration is used to measure the mass flow of fluids
directly, without the need for external pressure, tem-
perature or specific gravity measurements.
The meter consists of two separate units connected by
12-core cable: the flowtube, which is placed in line
with the process piping, and the transmitter, housing
the drive circuitry and the microprocessor which per-
forms the measurement calculations. A simplified dia-
gram of the flowtube is shown in Fig. 8. In practice the
flowtube is encased in a protective covering.
Strictly speaking there are two separate measurement
subsystems: the temperature subsystem consisting of
an RTD buried in the central mass or manifold, and the
mass flow/density subsystem which consists of the two
vibrating, serially connected, large bore tube loops
positioned in parallel. The tube is anchored to the rigid
central mass. The process fluid is pumped through the
central mass, through one tube loop back into the
central mass, and then through the second tube loop
such that the fluid flow is parallel to that in the first
Two electromagnetic drivers and two electromagnetic
velocity sensors bridge both loops at opposite extremi-
ties, equidistant from the centre. The former drive the
tubes with two 180° out of phase sinusoidal waveforms
at the damped resonant frequency of the vibrating
system, so that both loops rotate about AB. A feedback
system in the transmitter modifies the drive currents in
order to maintain a constant sensor voltage amplitude.
Coriolis forces act on the pipe sections parallel with
CD, causing tube deflections. The instantaneous veloc-
ity between the two loops is measured by the sensors
adjacent to the drivers. The measured phase angle
between the sensor voltages is proportional to the mass
flow rate of the fluid in the flowtube, while the density
of the process fluid can be obtained from the frequency
of oscillation of the system.
3.3. Sensor Self-Diagnosis (SSD)
Fig. 8. Coriolis Meter Flowtube
An increasingly common feature of digital sensors is a
self-diagnostic capability. In-built processing power
can exploit all the device-specific knowledge available
to the instrument designer. While MAD detects faults
based solely on observing the behaviour of the meas-
urement signal from outside of the sensor, Sensor
Self-Diagnosis (SSD) takes place within the sensor,
where there are typically many signals other than proc-
ess measurements, and indeed other sources of knowl-
edge, which can be used to monitor the health of the
device. These other sources may include device-spe-
cific tests that typically stimulate the transducer and
obtain diagnostic information from the response.
For example, within the Coriolis meter there are many
parameters of potential use for fault detection, includ-
ing internal signals, the amplitudes of harmonics, and
physical and electrical properties. Only the measure-
ment signals themselves are available outside the de-
vice, and these are far from ideal for detecting sensor
faults precisely because they are designed to convey
information about the process, not the sensor. The
graphs in Fig. 9 illustrate this point.
A certain condition can arise within a process which
can have a severe but temporary impact on the meas-
urement capability of a Coriolis meter. Fig. 9 show the
results in lab trials of this process-induced fault upon
the mass flow rate and L, a physical property calculated
using the output of a device-specific hardware test. The
L test is designed to show proper mechanical working
of the meter, and is relatively immune to most changes
in process conditions, including the mass flow. These
minor process effects are easily modelled, resulting in
a narrow range of permitted values of L shown by the
dashed region on the graph in Fig. 9a. However, the
test is extremely sensitive to even small levels of the
fault, which can thus be detected easily.
The effect of the fault on the mass flow rate reported
by the sensor can be seen in Fig. 9b. The true mass flow
rate in the rig was constant throughout the series of
experiments, and so we may deduce that the fault
causes a gradual lowering of output value which be-
comes more severe as the level of fault increases.
However, in a real plant, this drifting behaviour might
easily be mimicked or masked by genuine changes in
the process mass flow. If the measurement was a
controlled variable, then any drop in sensor output
value due to the fault would quickly be masked by
control action.
A measurement signal is designed to convey informa-
tion about the process, and therefore any method of
sensor fault detection based solely on measurement
information is inherently process-dependent. Applica-
tion-specific schemes must be devised, involving proc-
ess modelling which is expensive and which cannot
guarantee results. However, assuming in-built process-
ing power to perform self-diagnostics, it is reasonable
to assert that additional signals (such as L) can be
identified (or be specifically designed and generated)
within the sensor that can give indications of faults
irrespective of the behaviour of the measurand. Indeed
L is an esoteric example; trivial threshold logic on
signal levels within the sensor can detect faults which
may be difficult or impossible to trace within the
measurement, as is demonstrated later. If tests or sig-
nals such as these can be found or designed, then the
same fault detection algorithms can be used in all
identical sensors, without regard to the processes in
which they are installed.
It would be naive to suggest that comprehensive inter-
nal diagnostics can be generated with equal ease over
the entire range of sensing devices, from the thermo-
couple to the process analyser. However, it is surely
the case that in every sensor simple checks can be
implemented in order to reduce the possibility of an
undetected fault affecting an on-line measurement.
Economically this approach is appealing. The cost of
developing internal diagnostics for a single product can
be spread over the thousands of devices sold to indus-
try. Indeed as many of the requirements (test rigs,
skilled personnel) are retained by instrument vendors
in order to develop new products, it would appear that
the most cost-effective way to incorporate diagnostics
within a sensor is as a natural extension of the design
and development process. It is economically viable to
00.5 1 1.5 2 2.5 3
Degree of Fault
Bounds on non-faulty value
00.5 1 1.5 2 2.5 3
Degree of Fault
Reported Flow (kg/s)
Fig. 9. Effect of fault on (a) L (b) mass flow rate
emulate all known fault modes, even if the sensors
under test are destroyed, because the unit cost is nor-
mally small. Furthermore customer feedback can pro-
vide an excellent means of reviewing and improving
fault detection strategies, particularly if sensors with
unusual or unexpected faults are returned to the vendor
for detailed analysis.
A self-diagnosing capability is of course just one of the
enhancements achievable in a microprocessor-based
instrument, but it is becoming increasingly common in
new instruments. This trend is set to continue. Th e fault
detection community has, however, been slow to rec-
ognise and adapt to these developments.
SSD should be seen as a more powerful version of
MAD. It is more reliable because it is based upon
manufacturer’s knowledge and takes place within the
device where faults can be observed in a variety of
ways; with MAD one must merely hope that faults will
manifest themselves in the form of statistical anoma-
lies in the measurement signal. In theory self-diagnos-
ing devices should be a welcome advance, as all the
hard work has been done by the manufacturer.
The problem lies in integrating this local fault detection
capability into a more general scheme. The only sensor
data required by a monolithic FDI system is the ana-
logue measurements, which are already provided by
the control system (see Fig. 7). The transfer of diagnos-
tic information between the MAD monitor for each
sensor and the central fault detection system can there-
fore be specified and implemented by the FDI system
designers themselves. While this is expensive, it is at
least feasible. When diagnostics are performed within
the sensor itself, how is fault information to be con-
veyed to higher levels? The solution to this problem is
offered by the second major development within proc-
ess instrumentation.
3.4. Digital Communications
In-built processing power facilitates separate estima-
tion of the measurand and of the sensor fault state.
However, these data cannot be transmitted separately
over a single 4-20mA analogue channel. Operator in-
spection of the transmitter, perhaps via a hand-held
terminal, is barely adequate. The transmission of mul-
tiple parameters over a single pair of wires, for routine
use by automatic control/alarm/safety systems requires
a digital communication link (see Fig. 10).
Digital communications offer many other advantages
such as two-way data/command flow and remote re-
configuration of devices. One particular benefit is the
reduction of measurement error due to data transmis-
sion. Figure 11 illustrates the progression from ana-
logue to digital processing and transmission of sensor
data. At step (b), the incorporation of a microprocessor
(labelled uP) results in overall improvements in accu-
racy, as described in the previous sub-section. At step
(c), direct digital communication replaces the recon-
version of dat a into an anal ogue sig nal. This elimina tes
the errors (noise, non-linearities, discretisation) intro-
duced by the conversion process at each end. In par-
ticular, the precision limit imposed by the maximum
resolution of the D/A (and A/D at the other end) is
A number of proprietary digital communication proto-
cols are currently available, many operating over ex-
isting 4-20mA links, but digital communications will
become more widely adopted by industry with the
completion of the Fieldbus standard (Wood 1991). All
major suppliers and several leading users are co-oper-
ating to complete and demonstrate the IEC standard as
soon as possible.
The vendor-independent Fieldbus standard provides a
platform for rich message-passing between intelligent
components of a plant fault detection scheme. By de-
fining suitable data and command standards, the full
potential of intelligent devices can be realised. Sensors
and oth er devices can send fau lt informatio n along wit h
measurement data, and higher-level fault-detection
software can return commands to reconfigure, perform
additional self-checking, re-calibrate, or to log calls to
maintenance (see Fig. 12).
However, the Fieldbus committees have given little
consideration to the standardisation of fault informa-
tion messages. Existing fault detection schemes using
digital communications typically employ a single bit
Estimate of Measurand
Sensor Fault State
Enables transmission
of multiple parameters
Fig. 10. The intelligent sensor using digital communications
Transducer Analogue
Circuitry V / I 4 - 20 mA
4 - 20 mA
Transducer A / D u P D / A V / I
Transducer u PA / D Digital Communications
Fig. 11. Data transformations within the sensor:
a) purely analogue
b) microprocessor-based with analogue communications
c) microprocessor-based with digital communications
to indicate whether a measurement datum is ‘valid’ or
not. Alternatively, the device-specific error code (e.g
Fault 43 - detached transducer), generated by a self-di-
agnosing sensor, is passed to the higher level system.
The chief objection to using a single bit is that the
sensor must judge, on behalf of the control system,
whether a measurement is acceptable. The only choices
are yes or no. However, a fault may have only a
marginal effect on the measurement. A yet more diffi-
cult situation occurs when a number of measurements
are combined in some way to generate a result. If one
of the input measurements is flagged as bad, should the
result be?
For the purposes of monitoring and controlling the
process, a ‘bad’ measurement cannot be used, in which
case there is no point in generating any measurement
data at all while the fault persists. Experience suggests,
however, that operators at least would rather have ‘bad’
data than none at all. On the other hand, if a bad
measurement is used anyway, there is little point hav-
ing the fault flag. It is therefore likely that a one-bit
scheme will cause an unwarranted reduction in the
availability of the measurement. Some scale of meas-
urement degradation might be preferable.
If error codes are employed, then the problem of judg-
ing the utility of the data is passed upwards to the
control system: it is told what is wrong with the sensor,
and it must interpret the consequences of this particular
fault for the viability of continued operation. The major
problem here is the device-specific nature of such error
codes. The control system must be able to respond to
any of tens of fault modes for each sensor type in the
plant. This is unlikely to please the control engineer,
for whom it raises the bleak prospect of updating a
massive database of individual device characteristics,
or substantially reconfiguring the system software,
whenever a sensor is installed or replaced.
Unless some device-independent standard for describ-
ing sensor faults is agreed and adopted, the bottleneck
of the 4-20mA analogue channel could be replaced by
a cacophony of device-specific fault codes and com-
A more philosophical issue is also raised by the poten-
tial of digital systems, concerning physical and func-
tional identity. The new capabilities of localised com-
putational power and digital communications mean
that the computational functions taking place in physi-
cal objects are no longer fixed. Theoretically, almost
any function can be performed anywhere in the plant,
the relevant data being sent over the Fieldbus. For
example, Fig. 12 shows the components of a typical
loop, consisting of sensor, actuator and controller,
communicating via a Fieldbus. The only functions
which cannot be located anywhere are those associated
with physical components: transducer data must be
generated from within the sensor, and the positioning
of the valve (or other plant component) must take place
within the actuator. The location of any data processing
is theoretically arbitrary. We could, for example, per-
form measurement calculation within the sensor, but
carry out sensor fault detection within the loop control-
ler (by sending all internal signals to the controller over
the Fieldbus). Similarly we could perform the control
algorithm within the actuator and dispose of the loop
controller completely. In such circumstances, what do
we mean by sensor, actuator or controller?
In practice, certain constraints remain. The Fieldbus
has a limited bandwidth. The self-diagnosing sensor
processes a large quantity of data which is summarised
in the measurement and diagnostic parameters avail-
able to other system components. Processing this data
outside of the sensor would place a heavy load on the
Fieldbus. In addition a large amount of contextual
knowledge will be required to interpret the data, which
will include proprietary knowledge that the sensor
manufacturer is unlikely to make freely available.
There is therefore no obvious advantage, and several
apparent problems, with exporting sensor data process-
ing to other network components.
The possibility of performing control calculations
within the valve is more attractive. A simple PID law
could easily be incorporated within a valve thus elimi-
nating the need for a loop controller. In applications
where more sophisticated control is required, a control-
ler which contains a large amount of proprietary
knowledge, perhaps incorporating such features as pre-
dictive or adaptive control, would still be viable as a
stand-alone device.
3.5. The SEVA Approach
The application of digital sensors and communications
offer enormous advances in sensor functionality. Po-
tential enhancements are so powerful, however, that
they threaten to distort the current understanding of the
roles and functions of devices, thereby making system
integration far more difficult. Fieldbus notwithstand-
ing, there is as yet no description of the generic sensor
(or actuator) that encompasses the extra capabilities
offered by digital technology. The SEVA approach
offers such a description.
Loop Controller
Data Commands
Sensor Actuator
Plant Data
Commands Measurements
Fault Data Plant Data
Commands Performance Data
Fault Data
Fig. 12. Control using Fieldbus and intelligent devices
One aspect required of any generic description of the
sensor is a standard, device-independent means of
communicating sensor faults. Indeed a single sensor
fault must be described in a number of ways: a fault
code or detailed description is required for the purposes
of maintenance, but for monitoring and controlling the
process, the important issue is the impact a fault has
upon the quality of the measurement. The most appro-
priate method of evaluating this impact is provided by
the techniques of uncertainty analysis, as described in
the next section.
In the discussion thus far, the emphasis has been upon
fault detection and improving sensor performance. As
yet there has been no consideration of measurement
validation, that is assessing the ‘quality’ of measure-
ment data. The science of metrology covers the domain
of measurement and its accuracy, and the principal tool
for examining error is uncertainty analysis.
The concept of Uncertainty was originally defined by
Kline and McClintock (1953). Every measurement has
an associated error, which is of course unknown. How-
ever a single uncertainty number is often needed to
express a reasonable limit on that error (ANSI 1985).
For any observed measurement M, the uncertainty in M,
wM , can be defined as follows: we assert that
Mtrue [ M wM, M + wM ] (1)
with a certain level of confidence (typically 95%). This
uncertainty is readily expressed in a relative form as a
proportion of the measurement (i.e. wM
M ).
A propagation rule exists for obtaining the uncertain-
ties of arbitrary functions of primary measurements
(Kline and McClintock 1953, ANSI 1983, 1985). For
example, for an arbitrary function R of variables x, y,
and z,
R = R ( x, y, z )(2)
the uncertainty of R is given by
2 =
2 +
This sum of squares form is derived from the Taylor
series, and assumes the independence of x, y, and z, that
their relative uncertainties are ‘small’, and that all
uncertainties are expressed at the same probability
level. For the remainder of this paper all uncertainties
will be quoted at 95% probability.
One of the uses of the uncertainty propagation formula
is to reveal particular circumstances which can result
in a higher or lower than expected level of uncertainty.
For example (Kline, 1985), if R is calculated from x
and y using the equation
R = x y(4)
The uncertainty in R, wR , is given by
R =
x y wx
x y wy
Suppose x =1.00 and y = 0.98, and the uncertainty in
both x and y is 1%; then the uncertainty in R is:
R =
0.02 0.01
0.02 0.01
= 0.700 = 70% (6)
Consider now a result R found from a variable z
through the formula
R =
1 + z
12 (7)
The corresponding uncertainty is given by
R = wz
2 ( 1 + z ) (8)
If z = 0.1 and the uncertainty in z is 20%, then the
uncertainty in R is only 0.91%.
On reflection these results should not be surprising. In
the first example two quantities of similar magnitude
are being subtracted: clearly this will increase the
relative error in the result. In the second, although z has
a large uncertainty, its influence on R is relatively
small. Of course for different values of x, y, and z the
impact of their uncertainties will vary. The usefulness
of uncertainty analysis is that it can quantify these
In the original paper by Kline and McClintock (1953),
uncertainty was conceived in the context of the single
sample experiment. Since then uncertainty analysis has
been widely applied, and has achieved the status of an
international standard for calibration and for the report-
ing of experimental results (ISA 1980; ANSI 1983;
ANSI 1985). Indeed, certain ASME journals require an
uncertainty analysis to be submitted with all experi-
mental data (Dean 1975).
In the domains of calibration and experiment, however,
repeated measurements are normally possible and usu-
ally necessary. As a result, the standard documents
usually partition uncertainty into bias and precision.
Bias is a systematic error assumed constant for a given
set of readings under the experimental conditions. Pre-
cision error is a random component which is assumed
to give a sequence of independent errors in the read-
ings, and subject to statistical estimation via repeated
measurements. Two uncertainty terms estimating these
components are usually combined to present a single
overall uncertainty value associated with a particular
result or calibration.
It must be stressed that it is not possible to associate
uncertainty with a rigorous confidence interval be-
cause bias is often based on judgement and/or past
experience, while precision is calculated using statis-
tics. Therefore no function of these two numbers will
have a rigorous statistical basis (ANSI 1985).
4.1. Sources of Uncertainty
Measurement error sources can be categorized into
three groups (ANSI 1985):
calibration errors;
data acquisition errors;
data reduction errors.
Calibration Error The calibration process exchanges
the large bias error of an uncalibrated or poorly calibrated
instrument for the smaller combination of the bias error
of the standard instrument and the precision error of the
comparison. This exchange of errors is fundamental to all
calibration processes. Clearly the measurement
technology employed in the instrument undergoing
calibration will have a fundamental influence upon the
magnitude of the calibration error, as well as any
subsequent calibration drift.
Calibration is also used to provide traceability to
known reference standards, through a hierarchy of
standards laboratories (see Fig. 13). Each calibration
in the hierarchy constitutes an error source, and so
uncertainty increases down the chain of calibration, but
at the same time traceability is established from the
standard to the individual instrument.
Data acquisition errors. Inevitably the sampling
process itself has a fundamental influence on the
uncertainty. Other error sources which may be present and
included in this category are environmental effects (e.g
changes in ambient temperature), and installation effects.
Data reduction errors. Calculations are performed on
raw data to produce measurement data in engineering
units. Other parameters may be calculated using these
measurement data. Errors in these calculations stem from
the use of curve fits, correlations and/or approximate
numerical methods, as well as loss of resolution (as
demonstrated in the first worked example above).
4.2. Uses of Uncertainty Analysis
Other than its primary role in the calibration of instru-
mentation, uncertainty analysis has been put to good
effect in the design and analysis of experiments. Kline
(1985) describes how uncertainty can be used to par-
ticular ad vantage i n the des ign and operatio n of expe ri-
mental rigs, by:
minimising instrument cost for a given output
identifying which instruments or procedures con-
trol the overall uncertainty, thus focusing atten-
tion on points where care is particularly impor-
designing instrumentation to minimise uncer-
checking against unknowingly entering a region
of the data hyperspace where there are large un-
certainties due to data reduction effects.
Moffat (1985) describes how uncertainty can be used
to perform simple static analytical redundancy checks:
two estimates of the sam e parameter ‘agree’ when they
do not differ by more than the root-sum-square of their
uncertainties. This principle can be extended to any
number of estimates. ANSI (1985) provides a method
for weighting multiple estimates of a parameter in
order to obtain a ‘best estimate’.
4.3. Uncertainty and Process Instrumentation
Uncertainty has in the past been used as a static analy-
sis, providing a single uncertainty estimate for an in-
strument. In such a calculation, ‘average’ or ‘typical’
National Bureau of Standards
Interlaboratory Standard
Transfer Standard
Working Standard
Measurement Instrument
Fig. 13. Typical calibration hierarchy (from ANSI 1983)
values are substituted for parameters which vary. How-
ever, for process instrumentation there can be consid-
erable variation in uncertainty over the ranges of the
measurand, process temperature and pressure, and
other influencing factors. Instrument manufacturers
often provide ‘accuracy’ charts (see for example Fig.
14) showing how ‘accuracy’ varies with the meas-
urand, but these are of limited use. They usually cite
performance data under reference conditions in a labo-
ratory, and in doing so underestimate the true uncer-
tainties of their instruments under process conditions.
This is partly due to the difficulties of gauging and
representing the impact of multi-dimensional process
conditions in graphical form, but also perhaps due to
the desire to cite favourable figures in order to gain
market advantage (a practice known as ‘spec-man-
ship’). For example, Cork (1989) discusses the short-
comings of manufacturer’s accuracy data for Coriolis
Where it is particularly important for the uncertainty
of a measurement to be kept within specified limits (for
example in legal metrology applications), the only way
to do this at present is to have an instrument certified
by a registered testing laboratory. In this process the
uncertainty of the instrument is assessed on a test rig
while varying the measurand and process conditions.
The certification of the instrument only remains valid
as long as the process conditions remain within the
limits under which the instrument was tested. This is
both costly and restrictive.
However, the demand for measurement quality assur-
ance is increasing, stimulated by environmental legis-
lation as well as the drive for product quality assurance,
and a means of assessing uncertainty on-line under all
process conditions would be a major step forward.
4.4. The SEVA approach
The SEVA approach envisages each instrument pro-
viding an on-line assessment of its own uncertainty
using in-built processing power and the experience and
expertise of the manufacturer. Factors involved in this
calculation include process noise, the A-D or V-F
convertor, calibration (including time since last cali-
bration to estimate drift) and the measurement calcula-
tion, as well as the presence of sensor faults. This
approach, like sensor self-diagnosis, is now both tech-
nically and economically feasible.
Returning to the concept of the single sample (Kline
and McClintock 1953), only a single measure of uncer-
tainty is proposed, as opposed to separate calculation
of bias and precision, for in the reality of on-line
industrial measurements the process variable and error
sequence both vary in a complex time-dependent man-
ner. Outside of a calibration procedure, measurements
cannot be repeated, and therefore the concepts of bias
and precision have limited merit.
In order to realise this approach it is necessary to
combine uncertainty analysis with fault detection
within the sensor. This is, however, a natural extension
of uncertainty analysis, as faults are simply another
source of error. The manufacturer must expand the
measurement model of the sensor to include the impact
of each potential fault upon the measurement value and
its uncertainty. How this is to be achieved is outlined
It may be argued that if ‘spec-manship’ is common
amongst competing manufacturers now, then the un-
certainty values generated by self-validating devices
are likely to be somewhat optimistic. The counter
argument is that the SEVA concept provides a frame-
work in which uncertainty data can actually be used.
Spec-manship remains possible when it is expensive to
verify accuracy claims directly in the laboratory and
difficult to spot greater than claimed uncertainty in an
installed instrument. Indeed, under such circum-
stances, asserted accuracy has only limited use as a
criterion for selecting instrumentation.
If, on the other hand, as a result of using on-line
uncertainty data, worse than claimed performance
from a sensor is demonstrated, then the manufacturer
is likely to suffer dire consequences. Rather than risk
such an eventuality, manufacturers are more likely to
redouble their efforts to improve the actual perform-
ance of their devices, confident that improvements c an
be demonstrated and therefore rewarded.
Parallels can be drawn between potential uses of un-
certainty data in process plant and existing uses of
uncertainty analysis in experimental rigs as described
by Kline (1985), and summarised above. Potential uses
range from design through to control and maintenance
of the plant:
During plant design a maximum uncertainty is
specified for each measurement in the plant. This
figure will take into account how each measure-
ment is to be used: whether for monitoring of
product quality, demonstration of compliance for
Fig. 14. Typical accuracy graph supplied with a commercial instru-
legal metrology or environmental protection,
feedback control or alarm purposes.
Purchasing decisions can be based on the lowest
cost for the required uncertainty performance.
When the plant is operational, sensor maintenance
and calibration can be scheduled to keep on-line
uncertainty within the specified limits. Statistical
process control can be implemented using the
same criterion. When a sensor fault occurs, the
impact on measurement quality is indicated by an
increase in uncertainty, which can be used to
decide the appropriate control system response.
Simple, static redundancy calculations based on
uncertainty intervals can be used to verify plant
performance. Finally, recorded data can be used
to demonstrate legal or environmental compli-
ance, as each measurement and uncertainty value
can be traced back to a known standard.
The role of on-line uncertainty in the SEVA framework
is described more fully later.
Previous sections have described how the output of the
analogue sensor has been used in increasingly ingen-
ious ways:- for plant monitoring, feedback control,
safety trips, and for process, plant and sensor fault
detection. The limitations of the analogue sensor are
apparent, but these very limitations have enabled all of
these activities to co-exist, and the 4-20mA standard is
easily understood and implemented.
The balance between these activities is now, however,
in jeo pardy. The curren t piecemeal attemp ts to enhance
one aspect of sensor functionality using digital tech-
nology can be at the expense of others.
For example, the advantages of embedding microproc-
essor technology within the sensor have been described
in sections 3.1 and 3.3. The resulting improvements
are, however, at the expense of attempts to extract
process and plant health indicators from the measure-
ment signal. Returning to Fig. 11, the analogue cir-
cuitry shown in (a) may allow the typically high-fre-
quency health-related components of the transducer
signal to pass through into the measurement signal.
However, the incorporation of a microprocessor shown
in (b) decouples the measurement signal from the
transducer signal. In this configuration, high frequency
components will only be passed on if the microproces-
sor samples the transducer output at a higher rate than
is strictly necessary for measurement purposes alone.
In practice, the sensor designer wishes to eliminate
every influence other than the measurand itself in order
to improve the measurement performance of the sen-
sor. Thus the health component in the measurement
signal from a digital sensor may be distorted, attenu-
ated or removed completely. In some instances plant
operators have been reluctant to accept digital instru-
mentation for this reason.
The fundamental problem is that workers in the various
fields appear to have little awareness of each other’s
achievements and aspirations, and so have not given
integration issues a sufficiently high priority.
Manufacturers developing internal diagnostics
within digital sensors have given insufficient
thought as to how sensor fault information is to be
utilised (by the control system or by higher level
fault detection systems), resulting in their genera-
tion of single bit or device-specific error codes.
Workers in the fault detection domain have been
slow to respond to the innovations of the sensor
manufacturers. They still view measurement in-
formation as the only source and carrier of fault
information, and therefore rarely distinguish be-
tween a fault and its impact upon the measure-
ment. There has also been insufficient considera-
tion of how fault detection is to be integrated into
process operation.
The drafting of standards for digital field commu-
nications has provided an excellent opportunity to
develop one or more generic models of extended
sensor functionality, incorporating such features
as the transmission of sensor fault or measurement
validity data. No such models have emerged.
Metrological standards have yet to respond to the
potential of intelligent instrumentation for gener-
ating on-line uncertainty, and the need to include
fault detection as one aspect of measurement vali-
It is clear, therefore, that t he varied require ments on the
sensor need to be integrated into a new and extended
conceptual model.
Must this new conceptual model include all of the
aspects proposed in this paper? It has been argued that
all facets are required:
Fault detection is required because safety, effi-
ciency and productivity cannot be guaranteed if it
is possible for the control system to act upon
faulty data.
Metrology is required because fault detection on
its own is insufficient to guarantee measurement
quality, as there are many other influencing fac-
tors such as measurement technology, calibration,
and process conditions. There is no viable alter-
native to uncertainty as a comprehensive index of
measurement quality, given, for example, the uni-
versality of instrument calibration and the central
role of uncertainty in the calibration process.
Digital systems are required to provide economic
access to the manufacturer’s expertise, which is
essential for thorough fault detection and the cal-
culation of uncertainty on-line. It is not conceiv-
able, for example, that uncertainty could be ade-
quately assigned if it were based solely on the
(faulty or non-faulty) properties of the measure-
ment, as provided by a conventional FDI and/or
MAD scheme. The proper assessment of meas-
urement uncertainty for the industrial sensor re-
quires a detailed, device-specific diagnosis which
in turn requires proprietary knowledge and access
to signals within the device. This is only possible
within digital sensors, which in turn require digi-
tal communications to transmit the results of local
diagnostics, measurement and uncertainty calcu-
lations to the control system.
6.1. Introduction
In this section a new model of the industrial sensor is
described. The model description consist of a number
of definitions, with additional text providing explana-
tion or illustration. This description is not prescriptive:
implementation and computational issues will be the
subject of future papers. The definitions proceed from
the most general to more specific requirements.
6.2. General Concepts
D1: The Self-Validating (SEVA) Sensor The self-
validating sensor is an information source. It has memory
and the capability for internal computation and digital
Comment For the rest of this section the term ‘sensor’
will imply a SEVA sensor unless otherwise stated.
D2: The Next Level Up (NLU) The sensor is at the
bottom of a hierarchy of processing levels. The sensor
interacts with higher level functions having particular
Control System This regulates the operation of the
plant to deliver the required performance.
Fault Detection System This monitors plant opera-
tion for indications of actual or impending failure in
any plant component. This is achieved both by detect-
ing inconsistencies between measurement data and by
interacting with self-diagnosing plant components.
Maintenance System This schedules maintenance,
repair, replacement and calibration of plant compo-
Compliance System This records plant data in order
to demonstrate compliance with safety and environ-
mental protection restrictions. It also flags any actual
or impending condition likely to break these con-
Collectively these systems are described as the Next
Level Up (NLU). The sensor provides data to, and
receives data and commands from, the NLU via a
digital communications system (see Fig. 15).
Comment Each system may be manual (i.e. its function
carried out by the operator) or automatic, implemented
separately or in a single software framework. Where
appropriate, reference to particular systems will be made.
D3: The Hierarchical Relationship The sensor as-
sumes that all data it receives from the NLU is correct.
It does not attempt to validate or reconcile this infor-
mation. Conversely, the NLU may accept or reject
sensor data. A sensor may, however, reject a command
which it cannot carry out.
Comment The NLU has a wide range of data available
to it from numerous sources, while the sensor’s view of
the process is localised. The sensor will therefore always
attempt to reconcile itself with the NLU. The exception
is for situations where the sensor is required to perform
an impossible task - for example to reconfigure its
measurement range beyond its capability. It is assumed
that the systems comprising the NLU (see Definition 2
above) co-operate so that the sensor is dealt with
Process Data
Fault DetectionControl
Next Level Up
Data Validity
Two-way Communication using Standard Formats for Commands and Data
SEVA Sensor
Available within the device:
Full Proprietary Knowledge
All Internal Signals
Device-Specific Tests
Fig 15. The self-validating sensor and the next level up
D4: The Role of the Sensor The sensor performs a
variety of tasks. From each task derives one or more
principals to guide the design and operation of the sensor:
Plant Operation The sensor provides measurement
information that is used to monitor and control the
process. It performs validation and provides validity data
for each measurement.
O1: maximise the availability of each measurement
O2: maximise the accuracy of each measurement
O3: provide validity data with each measurement
Maintenance The sensor is a piece of plant that must
be maintained and repaired.
M1: provide detailed diagnostic information to assist
M2: provide timely warning of impending sensor
System Integration The sensor is a component of an
information network: it must undergo installation,
commissioning, reconfiguration, and must communicate
with other devices in the network.
I1: conform to a standard interface between system
I2: minimise the device-specific nature of commu-
Process Fault Detection The sensor provides data
which can be used to detect faults in the process or plant
F1: provide a maximum amount of process and plant
health data
D5: The Sensor Boundary The sensor is bound by
its physical interface with the process on one side, and
its communication interface with the NLU on the other.
It may contain several transducers; these may be physi-
cally separate from the transmitter which performs all
data processing. Any computational function taking
place within the sensor has access to all data within the
sensor. Beyond the sensor, only certain data are avail-
able, to be obtained through standard request mes-
Comment This definition encompasses the simple
thermocouple-transmitter pair as well as more elaborate
configurations such as a multiple-transducer tank-gauge
system or the multi-measurement Coriolis meter.
Defining the computational boundary of the sensor in
terms of data access and standard functions or services
has obvious similarities with structured programming
concepts in computer science.
6.3. Sensor Data Types
D6: Sensor-generated data types The sensor gener-
ates several types of data which are made available to
the NLU (see Fig. 15):
Measurement Data providing an estimate of a proc-
ess parameter.
Alarm Data indicating that a process parameter is
exceeding pre-set bounds.
Diagnostic Data describing the health of the sensor.
Validity Data the validity of the associated measure-
Customised Process Data user-designed parame-
ters providing data on the health of the process or plant
in the vicinity of the sensor.
In addition, the sensor allows read and restricted write
access to device-specific configuration data such as
calibration constants, ranges, and so on.
Comment Each of these data types is now described in
more detail.
D7: Measurement Data A measurement is the best
estimate of the current value of a process variable. A
measurement must be generated at the required fre-
quency, even in the presence of a sens or fault. A single
sensor may generate multiple measurements.
Comment The calculation of a measurement in the
presence of a sensor fault is described later.
D8: Alarm Data The sensor can indicate to the com-
pliance system that a measurand has exceeded, or is
anticipated to exceed, some pre-programmed limit.
Different alarm levels can be used to indicate the
severity of deviation. Limits are likely to be set for
reasons of safety or environmental protection.
D9: Diagnostic Data The sensor generates detailed
device-specific diagnostic information to assist in
maintenance or repair. Additionally, a summary of the
current or anticipated state of the sensor is generated to
enable maintenance scheduling.
D10: Validity Data Each measurement has an asso-
ciated validity. This indicates the degree of confidence
or belief to be ascribed to the measurement. If a fault
occurs in the sensor, the associated validity conveys
the reduced confidence in each measurement. There
are two complementary aspects of measurement valid-
Accuracy Taking all relevant factors into account,
how close to the true process value can the measure-
ment be asserted to lie?
Normality It is required by Definition 7 that a sensor
should provide measurement information even in the
presence of a sensor fault. The NLU must be made
aware of how ‘normal’ the circumstances were when
the measurement was generated.
Comment For example, if the transducer is damaged
and no longer responds to process changes in the
nominal manner, then the measurement may no longer
be suitable as a controlled variable, but may still be
useful in some secondary capacity. The components of
accuracy and normality are largely orthogonal. Clearly
a sensor in less than nominal condition will have de-
graded accuracy. Equally, however, two sensors meas-
uring the same process variable, but using different
techniques, will have different accuracies even when
both are in prime condition.
D10 (continued) Reflecting these dual aspects of
validity, there are two indices of validity associated with
each measurement - Uncertainty, and Measurement Value
Status - representing the accuracy and normality of the
measurement respectively.
Measurement Uncertainty The uncertainty of an
individual measurement generated by a SEVA sensor
combines bias and precision terms into a single value. It
is calculated based upon the uncertainties of the raw data,
the calibration process, data processing, and any other
relevant factors. It is recalculated each sample to take into
account time-varying influences.
Measurement Value Status (MV Status) The
Measurement Value Status indicates the circumstances in
which the measurement was generated, and takes one of
the following set of values : SECURE, CLEAR,
Comment The interpretation and assignment of MV
Status values is described later.
D11: Customised Process Data Optionally, the sen-
sor can provide additional computational facilities ena-
bling the user to process raw transducer data to gener-
ate additional parameters related to process or plant
health. The responsibility for validating such parame-
ters lies entirely with the user.
Comment Current trends, such as the increasing costs
of installation and maintenance, the decreasing cost of
computational power, and the widening application of
microsensors, suggest that the optimal design for process
instrumentation may change radically. Flexible, modular
multi-measurement sensor systems, designed to extract
as much data as possible from a single breach of the
process piping could become both technically feasible
and economically attractive. Such a system (Fig. 16)
might contain many transducers, measuring both physical
and analytical parameters, users installing only those
software modules needed to generate the measurements
they require. In addition users might generate their own
process or plant health-related parameters based on raw
transducer data via programmable filters, neural nets or
other data reduction processing units (see Fig 17). In this
way the measurement and health data could be generated
from the same transducers without interfering with each
other. Certain transducers (e.g. acoustic) could be
provided solely for generating customised parameters.
These parameters could be discrete (e.g. flagging some
plant or process change), or continuous (e.g. estimation
of degree of wear).
Process Piping
Transmitter Housing
Block 1
Block 2
Block 3
Block 4
Not in Use
Not in Use
Transducer Housing
Fig. 16. The modular sensor system
Decision Logic
processing block
Neural Net
Digital Filters
Calculation Block
Process Parameters
(Available to NLU)
Fig. 17. Example of processing unit generating one or more
customised process parameters
6.4. Sensor Diagnostics
D12: Sensor Fault A sensor fault is an aberration that
impairs any aspect of sensor functionality. A fault is
device-specific: it refers to the underlying cause of the
problem rather than any symptoms (such as might be
observed on the measurement signal). Faults may be
either discrete (i.e. either present or not), or have an
associated degree of seriousness.
D13: The Diagnostic State The sensor constantly
updates its internal assessment of its condition and
performance. The resulting diagnostic state lists all faults
that are judged to have occurred or to be imminent, with
their estimated degrees where appropriate. The diagnostic
state of the sensor determines how all output parameters
are to be generated for the current sample.
Comment The diagnostic state is the sensor’s best
estimate of its condition, and is subject to correction by
the NLU (e.g. in the form of a maintenance engineer).
Competent self-diagnostics should normally ensure that
the diagnostic state is reasonably accurate. Notifi cation of
the diagnostic state is supplied to different components of
the NLU in different ways, reflecting different aspects of
the sensor’s functionality.
D14: Raw Measurement Value (RMV) A Raw
Measurement Value (RMV) is a measurement value
calculated on current raw data under the assumption that
there are no faults present.
D15: Raw Uncertainty (RU) The Raw Uncertainty
value associated with a RMV is the corresponding
uncertainty of the RMV, taking all relevant factors into
account other than the presence of faults.
Comment The RU is typically calculated using
equations derived from the measurement calculations.
Uncertainties of calibration constants are quantified
during the calibration process.
D16: Validated Measurement Value (VMV) A
Validated Measurement Value (VMV) is the
measurement value representing the sensor’s best
estimate of the measurand, given the current diagnostic
D17: Validated Uncertainty (VU) The Validated
Uncertainty is the uncertainty of the VMV.
D18: Diagnostic Information Sources There are a
variety of diagnostic information sources available within
the sensor.
Raw data The first signal available in the device,
generally an electrical image of a transducer output, for
example a frequency, voltage or resistance.
RMV data Signals which correspond to process pa-
rameters e.g. mass flow or temperature.
Auxiliary data Signals within the sensor which are
not directly related to the measurand.
Device-specific tests Data from active self-testing
by the sensor.
NLU inputs Inputs from the Next Level Up, gener-
ally discrete values and thresholds relating to expected
process behaviour.
Application knowledge base Information relating
process conditions to likely fault behaviour.
Comment Each component is described in more detail.
D19: Raw data Raw data contain the maximum
information available on the transducer response and are
therefore a very rich source of information for statistical
D20: RMV data Most fault detection techniques in the
literature are based largely upon detecting aberrations in
measurement signals (either on an individual or a
collective basis). Within the SEVA sensor such techniques
can be applied to the RMV in order to provide one aspect
of diagnostic information.
D21: Auxiliary Information Auxiliary information is
provided by the potentially large number of other signals
within the sensor which are not directly related to the raw
data or RMV, but which can give useful information about
the health or performance of a sensor. These might include
the electrical properties of components within the sensor,
signal levels at the input or output stages of power
amplifiers, or hardware errors. Associated with the
auxiliary information may be statistical or other tests to
identify characteristic behaviour. Hardware errors are a
special, preprocessed form of auxiliary information
generated by digital components within the sensor,
requiring little or no processing or interpretation. They
generally map directly and immediately to a physical
diagnosis (e.g. memory checksum error).
Comment All of this auxiliary data is highly
device-specific and best defined by, or in close
consultation with, the instrument designers.
D22: Device-specific tests In addition to monitoring
auxiliary data, the sensor may be able to actively perform
device-specific tests. These typically stimulate the
transducer or other sensor component and judge health
by observing the response.
Comment Again, such tests are best implemented by
the sensor designers. Artificial stimulation of the
transducer may, of course, result in a ‘faulty’
measurement: it is dealt with in the same way as any other
fault as described later.
One difficulty associated with localised validation is
that there may be situations where the sensor has
insufficient information to be able to come to a valid
conclusion, for example in distinguishing between cer-
tain types of sensor fault (e.g calibration drift) and
legitimate process changes. The sensor must therefore
be able to appeal to the NLU (which has access to
additional information such as data from other sensors)
for clarification.
D23: NLU Inputs The sensor can ask for information
about process behaviour (e.g. anticipated process limits)
through standard requests to the NLU. In addition the
NLU may provide warnings of changes in process
behaviour which can be used to minimise false alarms.
D24: Application knowledge base The
manufacturer’s accumulated knowledge of conditions
likely to result in particular faults should be made
available to the sensor’s diagnostic considerations. The
data may include factors such as time since last calibration
and maintenance.
Comment In some applications sensors are known to
degrade much more rapidly under certain conditions, e.g.
at extremes of operating range. Such knowledge
potentially allows a sensor to estimate a ‘‘wear and tear’’
effect on its performance. For example, if a normal range
pH probe is exposed to more than about 12 pH for as little
as an hour it may become alkali-conditioned and fail to
respond when the solution becomes more acidic.
D25: The Diagnostic State Machine (DSM) The
Diagnostic State Machine (DSM) is the software system
responsible for co-ordinating and resolving all diagnostic
information, as well as for scheduling self-tests and
requests for data from the NLU.
Comment DSM complexity may vary widely
depending upon the sophistication of the sensor. A variety
of diagnostic techniques can be used within the DSM,
including FDI, signal analysis, and neural nets. The
highest level of the DSM is likely to contain a
knowledge-based element in view of the heuristic nature
of the decisions that must be taken.
D26: Steps Taken Each Sample Figure 18 illustrates
the steps taken each sample within the sensor. Raw data
is sampled from the transducer(s) and propagated through
the measurement equations to generate the RMV(s) and
the corresponding RU(s). Statistical and/or other feature
extraction processing is performed upon the RMV(s) and
the raw data. Other diagnostic data, as described in
Definition 18 above, may also be subject to statistical or
other processing, either every sample or on an occasional
basis. All of this data is passed to the DSM which updates
the current diagnostic state. Based on this the output
parameters are calculated: each VMV with associated VU
and MV Status, Alarm Outputs, Device Status and
detailed Diagnostic Data.
D27: Calculation of VMV and VU Any of a number
of strategies may be used to calculate the VMV and the
VU, given the current Diagnostic State, as listed below in
decreasing order of preference. Each sample one method
is selected for each measurement generated by the sensor.
Different strategies may be used for different
measurements, reflecting the different impact the current
Diagnostic State may have upon each measurement. A
fault, diagnostic test or other condition (e.g. time since the
last calibration) may have a detrimental effect upon a
measurement: in the following descriptions, the
underlying cause is less important than the resulting
confidence in the RMV.
Nominal confidence in RMV If there is no fault or test
condition which has any impact on confidence in the
RMV, then the VMV is set equal to the RMV and the VU
is set equal to the RU.
Reduced confidence in RMV A fault or test which has
only a partial impact upon the measurement process
should be accommodated. This means that the VMV and
VU should be assigned corrected values. The method of
correction depends upon the degree of understanding of
the impact of the fault or test upon all aspects of the
measurement process and calculation. Any introduction
of bias into the (partial) measurement should be corrected;
any increase in uncertainty should result in a
corresponding increase in the appropriate uncertainty
Diagnostic Data Alarm Data MV StatusVMV and VU Device Status
Alarm Flags Select
MV Status(es)
Update Diagnostic State
Calculate Statistics
and/or Pattern Match
Device Status(es)
advice for
VMV(s) and VU(s)
RMV(s) and RU(s)
Get Raw Data
Get other
Diagnostic Data
Fig 18. Steps taken each sample
variable in the measurement calculation. Such corrections
should take place as far back as possible within the
measurement calculation. If necessary the entire
measurement calculation must be reperformed
incorporating all corrections. The VMV and VU are then
set equal to the corrected RMV and RU values.
Comment The following examples illustrate the range
of fault accommodation strategies that may be applied.
A fault has the effect of changing the property of
the transducer, so that the ‘true’ value of one of
the calibration constants changes. Calculation of
the measurement based upon the normal calibra-
tion constant value will result in a biased meas-
urement. Supposing this fault to be well-under-
stood by the manufacturer, the sensor is able to
estimate a correction factor for the calibration
constant based upon diagnostic data (e.g. auxil-
iary data or a device-specific test). As this correc-
tion is more approximate than the normal calibra-
tion procedure, the uncertainty of the corrected
calibration constant is increased accordingly. Re-
calculation of the measurement data will result in
corrected values of the VMV and VU.
Another fault, although detectable, has an impact
upon the measurement which is poorly under-
stood (due to its rarity and/or the expense of
analysing the relationship between the fault and
the measurement). Based upon a heuristic assess-
ment, the RMV is still considered to be the best
estimate of the measurement (so the VMV is set
equal to the RMV), but the VU is set equal to the
RU plus an extra uncertainty factor to accommo-
date the fault.
Clearly the first example is a far more satisfactory
response to the presence of a fault than the second. This
illustrates the possibility of assessing and/or improving
instrument performance on the basis of measurement
quality in faulty, as well as non-faulty conditions. In
general a manufacturer might be expected to improve
the detection and accommodation of faults and other
conditions as experience accumulates over the lifetime
of a particular product.
D27 (continued)
No confidence in the RMV A test or fault may have
the effect that the RMV bears no relation to the process
measurand. In such circums tances the VMV is calc ulated
from historical data, and the VU includes the additional
uncertainty entailed in extrapolating past behaviour to
predict the present value. There are two types of historical
Short-term history - the behaviour of the meas-
urand immediately prior to the onset of the impair-
ing condition;
Long-term history - the process knowledge accu-
mulated by the sensor such as limits on measurand
value, maximum rates of change, and charac-
teristic process behaviour.
This data is used to provide a best estimate of the
current measurand value, with associated uncertainty.
Comment Faults of this type include those in which the
transducer has entirely ceased to function or has become
disconnected from the transmitter. Similarly, if a test
stimulates the transducer, thereby completely obscuring
Estimate of Measurand
0 5 10 15 20 25 30 35 40 45 50
-100 -50 0 50 100 150 200
Time (s)
Estimate of Measurand
0 50 100 150
Estimate of Measurand
0 5 10 15 20 25 30 35 40 45 50
-100 -50 0 50 100 150 200
Time (s)
Estimate of Measurand
0 50 100 150
Estimate of Measurand
0 5 10 15 20 25 30 35 40 45 50
-100 -50 0 50 100 150 200
Time (s)
Estimate of Measurand
0 50 100 150
Fig. 19. Example of measurement and uncertainty estimation using
historical data:
(a) Short Term Process History;
(b) Long Term Process History;
(c) combination of (a) and (b) using uncertainty calculus.
the process-related signal, then this strategy is appropriate
for the duration of the test.
The calculation of the VMV and VU is these circum-
stances can be performed using varying degrees of
sophistication for the process modelling. A simple
strategy is presented here by way of example. This
strategy combines two estimates of the measurand and
its uncertainty, based on short-term and long term
historical data, using the rules of uncertainty calculus.
Figure 19 shows the resulting short-term and long-term
estimates and their combination from a simple simula-
tion. In Fig. 19, the unbroken line is the estimate of the
measurand, as denoted on the left hand scale, the
dashed line shows the uncertainty, denoted on the
right-hand scale, and the shaded area shows the uncer-
tainty interval around the estimated measurand.
In the simulation, the measurement is fault free, with a
value of 60 units and an uncertainty of 5 units, until
t=5s. At this point a fault condition occurs which
reduces the confidence in the RMV to zero.
Figure 19(a) shows the strategy using short-term proc-
ess history. The estimate is set equal to the last fault-
free measurement, and the uncertainty is increased by
the maximum observed rate of change of the process.
In other words, this strategy asserts that the best esti-
mate of the measurand is where it was last seen, and
that it is unlikely to change from this value by more
than the previously observed maximum rate of change
of the process, which in the simulation is 3 units/s. This
strategy is of course both heuristic and pessimistic, as
probabilistically the measurement is likely to change
more slowly.
Figure 19(b) shows the strategy using long-term proc-
ess history. In the absence of a current estimate of the
measurand, 95% confidence limits on its value can be
calculated based upon long-term history. In Fig. 19(b)
these limits are 0 and 100 units respectively. Once the
fault has occurred, the estimate of the measurand is set
to the average historical value (in this case 50 units),
and the uncertainty is set to be half of the confidence
interval. In other words, this strategy asserts that the
measurement could take any value within previous
Neither of these strategies is entirely satisfactory. The
first works well in the immediate aftermath of the fault,
but as time proceeds the uncertainty interval may ex-
ceed the physical limits of the sensor and/or process
(e.g. include negative values). The second strategy is
unsatisfactory initially, as there is a sudden change in
the estimate of the measurand and an enormous in-
crease in uncertainty.
A better strategy is one which combines the estimates
generated by the first two. It is reasonable to assume
that the two estimates are independent, and so using
uncertainty calculus it is possible to combine them to
get a ‘better’ estimate (i.e. one with a smaller uncer-
tainty). Given estimates e1 and e2 with uncertainties
we1 and we2 , a better estimate is given by
ebest = we22
we12 + we22
e1 + we12
we12 + we22
Each estimate is thus weighted by the square of its
uncertainty. The uncertainty of ebest is given by
webest = we1 we2
we12 + we22
Figure 19(c) shows the results of combining the short-
term and long-term estimates. Immediately after the
fault the short-term estimate is more prominent; sub-
sequently the long-term estimate becomes dominant.
The value of the estimate slowly moves from the last
non-faulty value (60 units) towards the long-term av-
erage (50 units).
Of course there is no guarantee that the process meas-
urand is actually within any of these uncertainty limits
- particularly if the fault has been caused by some
massive process change. However, the sensor is re-
quired to provide a best estimate of the measurand,
with an associated uncertainty, and in the absence of
on-line transducer data it can only assume that current
process behaviour reflects historical trends. As the
NLU has access to other process data, it is better able
to determine whether this assumption is valid, and
whether therefore the sensor’s estimate can be trusted.
D27 (continued)
Combination of effects In cases where there is more
than one condition affecting a measurement, then all
effects are included, if possible:
If any condition causes the confidence in the
RMV to be zero, then the VMV and VU must be
calculated using historical data only, as described
above (i.e. this condition over-rides any causing
only reduced confidence in the RMV).
If two conditions cause reduced confidence in the
RMV, then both sets of corrections should be
applied. If the corrections are mutually exclusive
then the larger correction is applied.
D28: Calculation of the Measurement Value Status
Definitions 7 and 10 state that the sensor should generate
a best estimate of the measurand together with an
associated uncertainty, even in the presence of faults.
Definition 27 describes strategies for generating VMV
and VU values in the presence of one or more faults. A
change in uncertainty by itself gives no indication of the
extent or likely evolution of the conditions giving rise to
the change. In particular, the NLU may need to know
which strategy has been used to calculate the VMV and
VU. It is also important to know whether an abnormal
condition is expected to persist. This information is
conveyed by the Measurement Value Status.
Given two conditions :
1) Expected persistence of any abnormal condition:
long or short
2) Confidence in latest RMV: nominal/reduced/zero
The corresponding value of MV Status is shown in
Table 2. Table 2 MV Status Values
persistence Confidence in
RMV MV Status
not applicable nominal CLEAR
not applicable reduced BLURRED
short zero DAZZLED
long zero BLIND
Each measurement is assigned an MV Status value as
determined from the current Diagnostic State. There
are two additional status values:
UNVALIDATED: validation is not taking place.
SECURE: the VMV has been generated using
redundant measurements. The confidence in each
measurement is nominal.
Comment The status names were chosen by analogy
with vision.Typical examples of each are given below:
CLEAR The RMV signal structure is within normal
range for given process conditions.
DAZZLED The RMV signal structure is substan-
tially abnormal but there is insufficient evidence to
project future behaviour (typically spiky/outlier con-
taminated signal or initial response to a fault). Another
use of DAZZLED is when the sensor is performing
active self-testing which prevents reliable data from
being generated by the transducer(s). The length of the
test is likely to be known, and to be short. For the
duration of the test, all measurements which are com-
pletely impaired are set to DAZZLED.
BLURRED The RMV signal structure is abnormal
but still with reasonable correspondence to the meas-
urand (typically noisy or with distorted spectrum).
BLIND The RMV signal structure is so abnormal
that it bears no relation to the measurand (typically zero
variance or outside physical limits for significant inter-
val); the fault is expected to persist.
For example, Fig. 20 shows likely values of the MV
Status in the scenario shown in Fig. 19. Prior to the fault
the MV Status is CLEAR. At the onset of the fault, a
serious aberration is detected immediately, but the
cause, and therefore the persistence, of the fault cannot
yet be determined, and so the Status is set to DAZ-
ZLED. A little later, the DSM determines the nature of
the fault, which is both severe and permanent, and so
the Status is set to BLIND.
The MV Status may be used in selecting the appropri-
ate control strategy for a controller using the related
measurement. There are many possibilities for how
this can be done depending on the application. For a
simple loop a possible strategy is shown in Table 3.
Table 3 Control Strategy using MV Status
Status Operating Mode
DAZZLED Temporary freeze
BLIND Forced manual or alternative
The response to a BLIND measurement must almost
inevitably be seriously disruptive, and therefore this
Status value should only be adopted if there is strong
evidence that the condition will persist. DAZZLED
acts as an intermediary condition so that if a fault is
severe but turns out to be temporary (e.g. a spike error),
the MV Status sequence (CLEAR - DAZZLED -
CLEAR) will be far less disruptive, than if DAZZLED
was not defined (CLEAR - BLIND - CLEAR). Obvi-
ously if there is sufficient evidence within the sensor
that the fault is permanent as soon as it occurs, then the
Status is set to BLIND immediately.
D29: Calculation of Device Status The Device
Status is a generic, discrete value summarising the health
of the sensor. A single value is generated each sample. It
is monitored by the Fault Detection and Maintenance
systems. It takes one of the following values:
GOOD The sensor is in nominal condition.
TESTING The sensor is performing diagnostic tests
0 5 10 15 20 25 30 35 40 45 50
-100 -50 0 50 100 150 200
Time (s)
Estimate of Measurand
0 50 100 150
Fig. 20 MV Status behaviour corresponding to fault in fig. 19
which may be responsible for any loss of measurement
SUSPECT The sensor may be suffering from an
aberration; the condition has not yet been diagnosed.
IMPAIRED The sensor is suffering from a diagnosed
fault which has a minor impact on performance,
warranting a low priority maintenance call.
BAD The sensor is suffering from a diagnosed fault
which has a major impact on performance, warranting a
high priority maintenance call.
CRITICAL The sensor is in a condition that is
potentially dangerous, requiring immediate attention.
Comment It is stressed that that the (single) Device
Status refers to the health of the sensor, whereas the MV
Status refers to the quality of each (of one or more)
measurement. Normally there will be some correlation
between the Device Status and MV Status(es) - for
examp le, if the pri ncipal meas urement is B LURRED then
the Device Status is likely to be IMPAIRED.
CRITICAL is used to indicate that the sensor is in a
condition that may cause (or have caused) a hazard,
such as a leak, of the process fluid or of a dangerous
material (e.g. a reagent or a radioactive substance), fire
or explosion. This status refers only to hazards gener-
ated by the sensor itself, rather than by the process. The
following examples illustrate the different cases.
If the process temperature and/or pressure is so
high that the transducers are unable to operate,
then the Device Status is set to BAD.
If the process temperature and/or pressure is high
enough to damage nearby plant, but not the sensor
itself, then the Device Status may remain GOOD,
although the sensor may have separate alarm
functions programmed to flag such conditions.
If the process temperature and/or pressure is so
high that the sensor itself is likely to rupture, leak,
explode or catch fire, then the Device Status is set
to CRITICAL, even if the measurements them-
selves are perfectly functional.
Clearly only certain types of sensor are capable of
being hazardous. Detailed, device-specific diagnostic
data is also available for the instrument engineer.
An on-line PC-based prototype of a self-validating
Coriolis meter has been developed. This consists of a
conventional, commercially available flowtube and
transmitter, together with signal conditioning electron-
ics and a 386 PC. The software package is called
SIAMESE and is written in C++.
SIAMESE acts as a self-validating Coriolis meter:
Signals relating to both measurements and sensor
health are picked up from the transmitter.
Measurement and uncertainty calculations are
performed within the software. In non-faulty con-
ditions, the resulting VMVs (one each for tem-
perature, density and mass flow) show excellent
agreement with the values generated by the trans-
Uncertainty values are calculated based upon a
detailed uncertainty analysis of the instrument.
This analysis has been extended to model the
impact of the most important fault modes of the
A number of fault conditions can be detected.
During the presence of a fault, the VMV is cor-
rected and VU increased appropriately. On-site
metered trials have demonstrated the superiority
of the VMV values generated by the SIAMESE
system over the faulty values generated by the
A simple example is used to contrast the behaviour of
the conventional and validated instrument in the pres-
ence of a sensor fault. The fault demonstrated is the
loss of RTD input. Fig. 21 shows how the measure-
ments generated by the conventional Coriolis meter
behave when this fault occurs. Unsurprisingly, the
response of the temperature measurement is drastic: it
drops to about -110°C within a few seconds of losing
the RTD input. However, as the mass flow and density
measurements are calculated using the temperature
input, there are corresponding shifts in these measure-
ments, of about 7% and 20% respectively. At t=47s,
the temperature input is reconnected, resulting in a
return to fault-free operation.
It is reasonable to assume that any fault detection
system worthy of the name ought to be able to detect
this fault simply by observing the temperature meas-
urement. However, it is unusual for the Coriolis tem-
perature output to be used by the control system (par-
ticularly in a 4-20mA regime, where normally only one
measurement can be transmitted from the sensor).
More t ypically only the m ass flow or densit y values are
used. The more marginal biases in these measurements
are less clearly distinguishable from a genuine process
change or disturbance. This has already been discussed
at length in the introduction, as the fault shown in Figs.
3 and 4 is in fact the loss of RTD input (compare Fig.
21(c) with Fig. 3).
Even if the sensor has self-diagnostics, and is able to
detect the fault, without the benefit of uncertainty
analysis it is difficult to ascertain the impact this fault
has upon the mass flow and density measurements, and
therefore, assuming a choice of discrete flags, whether
they are sufficiently impaired to be declared ‘faulty’ or
Figure 22 shows how the SIAMESE program responds
to the fault. Note that only the mass flow output has
been drawn to the same scale as used in Fig 21: this
enables features of the response to be displayed more
There is an initial warmup period terminated by the
first change of status to CLEAR for each measurement.
The loss of RTD input is detected by observing an
internal signal. The detection has two stages. Initially,
there is a rapid drop in value of the signal, which is far
faster than could be generated by normal RTD opera-
tion. A little later the signal settles at a constant value
characteristic of RTD input loss, and hence the correct
diagnosis is deduced.
The measurements and their validities are calculated
based upon this two-stage diagnosis. When the initial
10 15 20 25 30
Time (s)
Validated Temperature (C)
0 2 4 6 8
Validated Uncertainty (C)
970 975 980 985 990 995 1000 1005
Time (s)
Validated Density (kg/m^3)
0 5 10 15
Validated Uncertainty (kg/m^3)
2.4 2.45 2.5 2.55 2.6 2.65
Time (s)
Validated Mass Flow (kg/s)
0 0.002 0.004 0.006 0.008 0.01
Validated Uncertainty (kg/s)
Fig 22. Response of SIAMESE system to RTD input loss
(a) temperature
(b) density
(c) mass flow
-150 -100 -50 0 50
Time (s)
Unvalidated Temperature (C)
900 1000 1100 1200 1300
Time (s)
Unvalidated Density (kg/m^3)
2.4 2.45 2.5 2.55 2.6 2.65
Time (s)
Unvalidated Mass Flow (kg/s)
Fig 21. Response of unvalidated Coriolis meter to RTD input loss
(a) temperature
(b) density
(c) mass flow
drop is detected, the temperature is set to DAZZLED,
and the VMV and VU are calculated using the method
described in Definition 27 above. Subsequently, at
t=14s, the loss of RTD diagnosis is confirmed and the
status of temperature is set to BLIND. The VMV and
VU are calculated in the same way as during the
DAZZLED stage. When the RTD input is reconnected,
a recovery mode is entered, during which the tempera-
ture is set to BLURRED.
The loss of the RTD input has an indirect impact upon
the density and mass flow, as shown in Fig. 21. Within
SIAMESE this is accounted for in the calculations of
their VMV and VU values by the propagation of the
temperature uncertainty through the measurement
equations. For example, the mass flow is calculated
using an equation of the form
M = (K1 T + K0) . ϕ
where K1 and K0 are flowtube calibration constants,
subject to uncertainties wK1 and wK0 respectively, ϕ is
the phase difference between the two motion sensors,
subject to uncertainty wϕ, F is the drive frequency,
subject to uncertainty wF, and T is the measured proc-
ess temperature, subject to uncertainty wT .
The corresponding equation for the uncertainty of the
mass flow rate is given by the following:
2 = (T wK1)2 + wK0
(K1 T + K0)2
+ K1
2 wT
(K1T + K0)2 +
2 +
When the fault in the temperature input is detected, the
impact on the mass flow can be calculated by propa-
gating the value of wT into this equation. As the tem-
perature uncertainty increases from about 2°C up to
7°C, the resulting uncertainty in the mass flow rate
increases from 0.008 kg/s up to 0.0095 kg/s. Under
different process conditions, the same fault may have
greater or lesser consequences
The Status of the mas s flow and density measurements
are selected based upon the current diagnosis. When
the reason for the temperature loss is unknown, these
Status values remain CLEAR. However, once the fault
is diagnosed, then it is judged that, as mass flow and
density have a secondary dependence upon tempera-
ture, their Status values should be set to BLURRED.
As soon as recovery begins, they are reset to CLEAR.
Assuming the temperature input is not so rapidly (and
artificially) restored, how might the different systems
comprising the Next Level Up respond to this fault?
The Control System is informed that the tempera-
ture has gone BLIND, while the other two meas-
urements have gone BLURRED with marginally
increased uncertainties. Any feedback control
based on temperature will have to be switched to
manual or another measurement. Continued use
of mass flow or density will depend upon appli-
cation-specific criteria, which may be specified in
terms of the generic parameters uncertainty and
The Fault Detection System notes the change of
Device Status from GOOD to IMPAIRED; any
local and temporary inconsistency between vari-
able detected by an analytical redundancy scheme
may be attributable to this fault.
The Maintenance System notes the change of
Device Status and schedules a low priority main-
tenance check. A detailed diagnosis is obtained
from the instrument to enable the engineer to take
appropriate action.
The Compliance System is able to judge, based
upon the increased levels of uncertainty, whether
any of the measurements infringe safety, environ-
mental protection, or legal metrology restrictions.
If so, it will indicate to the Control System that
further action is required.
The Siamese system is able to detect a growing number
of faults within the Coriolis meter (currently some 15),
occurring alone or in combination, the most important
of which detect more subtle faults than a simple loss of
input. However, even ‘trivial’ faults, like in the exam-
ple above, may have an important impact on measure-
ment output, and their detection can avert serious deg-
radation of plant performance.
This paper has argued the need for an extended model
of the generic sensor in order to accommodate increas-
ing performance requirements and extended capabili-
ties. A series of definitions have been provided to
describe the self-validating sensor, and a prototype
system has been presented. Clearly this is only a first
step in describing a new methodology of plant opera-
Future papers will examine the following topics:
Fault detection in the Coriolis meter;
The self-validating actuator: rationale, definitions
and examples;
The use of uncertainty data in Analytical Redun-
dancy schemes;
The relationship between faults and uncertainty;
The development of standardised strategies for
exploiting sensor validation information at the
loop and unit levels.
The authors gratefully acknowledge the support of
DTI, SERC, Foxboro Great Britain Limited, and ICI
PLC for the work described in this paper, carried out
as the SEVA project under the LINK initiative. Particu-
lar thanks are given to our colleagues in the SEVA
project: Paul Clarke, Doug de Sa, Milos Machacek,
Steve Walsh, Martin Ward, Terry Wilkins, Graeme
Wood and Janice Yang.
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... Applied to sensors, these aspirations align with the concept of the Self-Validating (SEVA) Sensor ( [6], a UK National Standard [7,8]), alongside the broadly equivalent notion of "metrological self-check" developed in Russia and elsewhere [9][10][11][12]. Here, the sensor (taken to include both the transduction and data processing elements) performs self-diagnostics, but further assesses the quality of its measurement data, applying corrections as required for any detected faults, and provides standardised data quality metrics to assist higher level systems evaluate the usefulness of the measurements for particular tasks. ...
... These data quality metrics include an on-line, dynamic assessment of metrological uncertainty. SEVA, originally conceived in the process industry context [6], is applicable to sensor networks generally, for example wireless systems [13]. The NAMUR NE107 standard [8,14], which has been widely adopted by the suppliers of process instrumentation, has the less ambitious goal of providing standardised diagnostic messaging for instruments of the same type across multiple vendors. ...
... This reduces the risk that any change in the actual process, for example in the measurement noise characteristics, is misinterpreted as a fault symptom. Typically [6], such fault detection entails the identification of additional signals within the transducer, or specially developed device-specific tests, which provide measurement-independent diagnostic information. For example, the current authors have developed a diagnostic technique for pressure sensors, whereby the mechanical integrity of the sensor housing can be checked by use of an internal ultrasonic transmit/receive transducer [23,26,27]. ...