ArticlePDF Available

Abstract and Figures

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.
Content may be subject to copyright.
Sensors 2011, 11, 3356-3380; doi:10.3390/s110303356
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Signal Injection as a Fault Detection Technique
Jordi Cusidó *, Luis Romeral, Juan Antonio Ortega, Antoni Garcia and Jordi Riba
MCIA Research Group, Universitat Politècnica de Catalunya, C. Colom 1, Terrassa, 08222 Catalunya,
Spain; E-Mails: romeral@eel.upc.edu (L.R.); ortegar@eel.upc.edu (J.A.O.); garciae@ee.upc.edu (A.G.);
riba@ee.upc.edu (J.R.)
* Author to whom correspondence should be addressed; E-Mail: jordi.cusido@upc.edu;
Tel.: +34-93-739-58-18; Fax: +34-93-877-73-74.
Received: 25 January 2011; in revised form: 28 February 2011 / Accepted: 10 March 2011 /
Published: 21 March 2011
Abstract: Double frequency tests are used for evaluating stator windings and analyzing the
temperature. Likewise, signal injection on induction machines is used on sensorless motor
control fields to find out the rotor position. Motor Current Signature Analysis (MCSA),
which focuses on the spectral analysis of stator current, is the most widely used method for
identifying faults in induction motors. Motor faults such as broken rotor bars, bearing
damage and eccentricity of the rotor axis can be detected. However, the method presents
some problems at low speed and low torque, mainly due to the proximity between the
frequencies to be detected and the small amplitude of the resulting harmonics. This paper
proposes the injection of an additional voltage into the machine being tested at a frequency
different from the fundamental one, and then studying the resulting harmonics around the
new frequencies appearing due to the composition between injected and main frequencies.
Keywords: fault detection; induction motor; electrical drives
1. Introduction
The history of fault diagnosis and protection is as old as machines themselves. The manufacturers
and users of electrical machines initially relied on simple protection against problems like overcurrent,
overvoltage, earth-faults, etc., to ensure safe and reliable operation. However, as the tasks performed
by these machines became more complex, improvements were also sought in the field of fault
diagnosis. It has now become very important to be able to diagnose faults at their very inception, as
OPEN ACCESS
Sensors 2011, 11
3357
unscheduled machine downtime can upset deadlines and cause enormous financial losses. The major
faults of electrical machines can broadly be classified as follows:
Electrical Faults:
1. stator faults resulting in the opening or shorting of one or more stator windings;
2. abnormal connection of the stator windings;
Mechanical Faults:
3. broken rotor bars or rotor end-rings;
4. static and/or dynamic air-gap irregularities;
5. bent shaft (similar to dynamic eccentricity) which can result in frictions between the rotor and
the stator, causing serious damage to the stator core and the windings;
6. bearing and gearbox failures.
and the frequency at which different kinds of fault typically occur is shown in Figure 1:
Figure 1. Statistical distribution of motor faults.
Operating a machine under faults generates at least one of the following symptoms:
1. unbalanced air-gap voltages and line currents;
2. increased torque pulsations;
3. decreased average torque;
4. increase in losses and decrease in efficiency;
5. excessive heating.
Many diagnostic methods have been developed for detecting such fault-related signals. These
methods come from different types and areas of science and technology, and can be summarized as
follows [1-4]:
1. Electromagnetic field monitoring by means of search coils, and coils placed around motor
shafts (axial flux-related detection). This is associated with the capacity for capturing the
presence of magnetic fields around an IM. Field evaluation must provide information about
motor-operation states as proposed by Zidat et al. [4], but this is an intrusive proposal.
2. Temperature measurements: temperature is a typical second-order effect in operation
conditions. Induction motors typically have an operational temperature range, defined in the
motor nameplate, and associated with tests performed. Any fault-operation condition shows a
41
37
10 12
Bearing Faults
Stator Faults
Broken Rotor Bars
Eccentricities
Sensors 2011, 11
3358
temperature increment. By performing a temperature analysis the first approach to identifying
fault conditions could be made.
3. Infrared recognition: this is used to evaluate the material state, especially for bearings. This
cannot be performed in an online system.
4. Radio frequency (RF) emissions monitoring: radio frequency is a second-order effect of a fault
condition, which is currently used for gearbox diagnosis.
5. Vibration monitoring: this is the typical method for fault diagnosis in industrial applications; it
achieves good results for bearing analysis, but presents some deficiencies with electrical and
rotor faults [5,6].
6. Chemical analysis: this is used to analyze bearing grease; it is used only with large motors and
not with the more typical small ones.
7. Acoustic noise measurement: this is a new trend in the field of gearbox failure detection.
8. Motor current signature analysis (MCSA), which is explained further below.
9. Model-based artificial intelligence and neural-network-based techniques. These are new
approaches which combine multi-modal data acquisition and advanced signal-processing
techniques introduced by Nandi et al. [7].
The present work is not an attempt to develop fault diagnosis for all recognized methods, but
instead focuses on the analysis of the motor current signature analysis (MCSA) technique. This
technique has been chosen for its recognition as an industrial standard and as a non-invasive technique.
The basis of this technique is widely known and has been introduced by several authors. Among them,
Toliyat et al. [7,8], Benbouzid et al. [9,10], and Thomson [11,12] are the most relevant in the field,
although many others [13-20] have also contributed to scientific advances in the area.
These publications introduce the basis of MCSA operations, which are also the basis of this
research project. Many of the authors deal with mechanical faults, especially with the effects of broken
rotor bars and eccentricities. Thomson, though, focuses on stator fault diagnosis and presents good
results and arguments. These works are a good introduction to MCSA condition-monitoring techniques
and give a clear overview of the analysis of faults in induction machines for steady-state operations.
Power supply in induction machines creates a rotating magnetic field on the armature. The rotating
magnetic field induces rotor voltages and currents at slip frequencies, and this generates an effective
three-phase magnetic field rotating at slip frequency with respect to the rotor. Two different cases
appear:
Symmetrical cage winding only forward rotating field is produced.
Asymmetric rotor a backward rotating field will result at slip frequency with respect to the
rotor.
This backward rotating field induces a voltage in the stator at the corresponding frequency, and
generates a related current which modifies the stator-current spectra. Different rotating fields appear
with different faults in the induction machine, such as air-gap eccentricity, broken rotor bars, bearing
damage and short circuits in the stator windings. The current frequencies associated with rotating fields
are expressed by Equations (1–4):
Sensors 2011, 11
3359
(a) Air-gap eccentricity fault [9,10]
±= p
s
mff secc
1
1 (1)
where m = 1,2,3,… is a positive integer, p is the number of pole pairs, s is the per-unit slip, and fs is the
electrical supply frequency.
(b) Broken rotor bars [7,8]
±
=s
p
s
lff sbdb
1 (2)
where l/p = 1,5,7,11,13,…are the characteristic values of the motor.
(c) Bearing damage [9,10]
o,isbng mfff ±=
±=
β
cos
pd
bd
f
n
fr
b
o,i 1
2 (3)
where nb is the number of bearing balls, fi,0 are the characteristic vibration frequencies, fr is the speed
of the mechanical rotor in Hz, bd is the ball diameter, pd is the bearing pitch diameter, and β is the
contact angle of the balls with the races.
Equation (3) shows the frequency vibration of a motor with a broken bearing; however these
harmonics cannot be easily appreciated on currents. In fact, the case of bearing damage causes rotor
eccentricity, and furthermore the appearance of eccentricity on the rotor or even on the load will cause
further bearing damage. For this reason, we can also use Equation (1) to detect bearing problems.
(d) Shorted turns
d. (1) medium frequencies
±= p
s
mZff ssth
1
12
(4)
d. (2) low frequencies
()
±= ks
p
m
ff sstl 1
(5)
where Z2 is the number of rotor slots or rotor bars and k = 0,1,3,5,...
Expression (4) shows the components produced by shorted turns in the air-gap flux waveform, and
hence the stator currents as a function of rotor slots, around the medium-order harmonics, while
Expression (5) shows the harmonics produced by the fault around the base frequency fs. However,
frequencies shown by (5) also appear in the case of any rotor unbalance, including eccentricities, rotor
misalignment, etc. Therefore, (4) is frequently used to detect the fault, and (5) is used to assure the
origin in shorted turns in the stator winding.
Figure 2 depicts the stator current spectrum of the induction machine. The harmonic frequencies
produced by the fault are clearly shown at 25 Hz, 75 Hz, 125 Hz and 175 Hz, as expected from (1).
Sensors 2011, 11
3360
Figure 3 depicts the stator current spectrum for a constant load of the induction motor with broken
bars, one-sixth of the total in this case. As expected, an important harmonic appears in the lower
sideband of the main frequency.
Figure 2. Stator current spectrum of an induction motor with high eccentricity at nominal load.
Figure 3. Stator current spectrum of an induction motor with eight broken bars.
The effects of electrical faults on induction machines are clearly introduced by Thomson [11,12],
while some other authors [17,18] work with current monitoring without spectral analysis. In the case of
stator faults, spectral analysis may not be needed. However, it is worth considering if we are aiming
for a global solution for the fault diagnosis of induction machines.
Having acquired this knowledge about motor behavior under healthy and faulty conditions and its
relation to the distribution of harmonics, deeper studies for improving fault detection could be carried
out. As previously described, MCSA is a good fault-detection technique, which has achieved good
results in numerous cases. However, its drawbacks do not allow a global solution for an online
condition-monitoring technique or the development of diagnostic tools.
Sensors 2011, 11
3361
The main drawbacks are related to the fact that induction machines do not operate with a constant
low torque and at a constant speed. Induction machines have become increasingly popular, especially
since inverter drives appeared on the market. Nowadays, squirrel-cage motors cover most industrial
and domestic applications and are the most important way of converting electrical energy to
mechanical energy. These motors work with different kinds of applications with constant and variable
loads, and at constant and variable speeds. Moreover, inverters introduce additional drawbacks in
motors, such as common mode voltages, dv/dt, and additional harmonics. A global solution is needed
and induction machines in different operating positions should be studied further. The main purpose of
this work is to develop new fault-detection techniques for any operating condition.
Different solutions have been introduced in order to minimize the problems related to proper fault
identification under non-standard load conditions. Some are based on flux measurement in the stator
teeth [21], or by performing higher-order statistical analyses [22].
Important trends in fault detection are the injection of additional frequency tests and the
development of new tools based on improved signal-processing techniques, such as the Wavelet
Transform or dq0 conversions. The first introduction of signal injection can be found in the EN
61986-2002 standard used for motor insulation evaluation. In 1998 Ho and Cheng [23] introduced the
low-frequency signal injection on faulty machines, which proved to be a good approach with some
very interesting results. However this is far from being a full solution, since it fails to take into account
the effects of the signal injection, such as the composition between injected and fundamental
harmonics.
In a paper published in 2004 [24], Henao, Capolino et al. developed the idea of mechanical fault
detection by injecting different excitation signals, such as a discrete interval binary sequence (DIBS)
and multisine, with the intention of exciting faulty modes with the low frequency resolution and
analyzing the stator current and the stray flux measured by an external flux sensor. This work,
regardless of being based on the analysis of stray flux, offers an interesting approach to faulty motor
behavior excited by different injected signals.
Two articles published in 2003 and 2004 [25,26], by Briz and co-workers, use high-frequency
injection as a method of detecting winding faults in the first paper, and rotor faults in the second. The
measurement of the negative-sequence carrier-signal currents, using low-magnitude high-frequency
voltage superimposed by the fundamental excitation voltage, was shown to reliably detect faults in the
stator windings and the rotor cage (broken rotor bars) at their incipient stage, regardless of the working
condition of the machine. This is also an interesting approach, which we have considered in our work,
although the effect of signal compositions has been not taken into account. These works [24-26] show
the injection of additional signals as a good technique for fault detection. However, the effects of
frequency composition and behavior under double frequency (injected plus fundamental) are not
clearly shown. These subjects are developed, and supported by theoretical analysis, simulations and
experimental results. As already introduced, injection can be a good method of analyzing motors
driven by power inverters, which could implement a diagnostic routine.
Sensors 2011, 11
3362
2. Proposed Approach
Due to the effects of induction we expect to see both the main frequency and the auxiliary
frequency injected in the spectrum. However, as a contribution of the magnetic nucleus and iron
hysteresis, and also due to the general non-linearity of the induction motor, additional compositions
appear, defined by the following equation:
isc fmfnf
+
=
(6)
where n = m = ...2, 1, 0, 1, 2, …, and fc > 0.
It is possible to determine the effect of broken rotor bars in the motor’s current spectrum by
studying the flux composition in the stator and the mechanical composition of frequencies as a speed
composition. In the stator there are different magnetic fields due to the different signal injections. If
different fields are considered as different wheels moving around themselves with different angular
speeds, relative speeds between them will become evident.
Moreover, if the rotor is taken into consideration, it will be easy to define the different relative
speeds between the rotor and all the stator fields. The relation equations between rotor currents and
stator currents in an induction machine establish the former as an image of the latter. For instance, if
the rotor has salients such as broken bars, these will have an effect on stator currents as images. In an
ideal induction machine, all the different current distributions will be sine-shaped like the fields, but
there are many effects that cause non-idealities. In addition, any change in the air-gap flux distribution
can be seen as a non-ideal effect and will cause some marks in the current spectrum, as well as around
the different injected signals.
To determine these different marks, it is necessary to study the composition of the different
frequencies, the different magnetic fields induced in the machine, and the relative speed between them.
In (7) we shall consider the rotational speed of the motor fr:
(
)
p
s
ff sr
=1
(7)
Broken bars or rings, fractures in the squirrel cage, and other faults in the rotor will lead to
pulsating fields, which can be seen as two rotational fields rotating at slip frequency:
srotational sff
(8)
From the point of view of stator windings, the backward component of the rotor bar failure is seen
at frequency (sfs + l fr), where l is the function of pole pairs. This means:
=s
p
s
lff sbackbb
1
_
(9)
corresponding to the broken rotor bars frequencies in the left sideband. Note that the forward
component of the rotating field in the rotor does not produce any new harmonic in the stator spectrum.
If a three-phase test signal is injected in the stator at frequency fi, new rotational components are
again produced in the rotor at frequencies
±
(fi - fc - fr), where fc are new composed frequencies such as
(6). The rotating image fields produced in the stator are seen at
±
(fc fr)
±
fr. A general expression can
be obtained that includes all the harmonics of the main and injected frequencies:
Sensors 2011, 11
3363
sisinj_back_bb sfjfmfnf 2
±
±
=
(10)
where j = 1, 3, 4, 6, ..
The faulty frequency components that appear in the stator are not only due to the injected signals,
but also to the composed frequencies specified by (6). Harmonic components produced by the failure
in the rotor are expected to be found around the composed and corresponding harmonics of these new
frequencies.
The motor could be considered as a low-pass filter with a pole frequency of 400 Hz. Since different
injected frequencies will produce different compositions, the injected signals should be chosen to
obtain composed frequencies between four times fs and 400 Hz. In this way, the optimum bandwidth is
windowed to analyze the stator current spectrum without affecting the motor operation.
The main (and sometimes the only) solutiion when a motor fails is to repair it or to replace it. On
the contrary, the approach presented allows setting up permanent supervision and predictive
maintenance actions on the motor and the associated chain. The way to implement the frequency
injection test is as simple as injecting frequency components from the inverter source and analyzing
frequency bands around the new harmonics appearing on the stator current.
3. Simulation Analysis
The objective of the preceding modeling was to estimate the impedance variation due to faults. The
typical parametric model for induction machines is presented in Equations (11), (12) and (13). They
express the voltage relationship between rotor and stator (11), torque (12), and speed and rotor position
Equations (13).
[]
[] [][]
[] [ ]
(
)
[
]
(
)
[
]
()
[]
()
[]
+
=
r
s
rrrs
srss
r
s
r
s
r
s
I
I
LL
LL
dt
d
I
I
R
R
V
V
θθ
θθ
0
0 (11)
[] ()
[][]
rsr
t
selec IL
d
d
ItT
θ
θ
=)( (12)
()() mmecelec
m
d
t
d
TT
J
d
t
d
ω
θ
θ
ω
== ;
1 (13)
3.1. Rotor Misalignment
Rotor misalignment can be expressed as a variation on mutual inductances between rotor and stator
windings. This variation pulses at the frequency s
fs referring to stator fields.
This means a variation on mutual inductances of:
Sensors 2011, 11
3364
(
)
=
+=
=
3
2
2cos
3
2
2cos
2cos
3
2
1
π
π
π
π
π
s
s
s
fskk
fskk
fskk
(14)
Giving a final expression of inductances:
(
)
(
)()
() () ()
() () ()
() () ()
() () ()
() () ()
+++
+++
+++
+++
+++
+++
SBRR
RRBR
RRRA
RSRSRS
RSRSRS
RSRSRS
SRSRSR
SRSRSR
SRSRSR
SBSS
SSBS
SSSA
LMM
MLM
MML
kMkMkM
kMkMkM
kMkMkM
kMkMkM
kMkMkM
kMkMkM
LMM
MLM
MML
CBCA
BCBA
ACAB
CCCBCA
BCBBBA
ACABAA
CCCBCA
BCBBBA
ACABAA
CBCA
BCBA
ACAB
132
213
321
132
213
321
111
111
111
111
111
111
(15)
3.2. Broken Rotor Bars
The incidence of broken rotor bars (BRB) must appear principally as a variation on rotor
resistances. In fact, BRB incidences produce changes in both rotor resistances and inductances.
However, for broken rotor bars, variations of resistance in one rotor phase allow proper results to be
achieved. The actual degree of error depends on the number of bars the rotor cage has, the number of
contiguous broken bars, and the damage in the degrading bar(s). Since Rra is the equivalent resistance
of parallel n/3 rotor bars, if all but one rotor bar are healthy then the relationship can be obtained by the
following Equation (16):
()
() ()
()
kn
nk
R
R
n
R
R
R
n
k
Rk
n
R
Rk
n
R
RR
ra
ra
rai
ra
rai
rai
rai
rai
rai
rara
+
==
=
+
=
+
==
33
3
13
1
13
13
'
'
α
α
(16)
For example, for a 12 bar in a rotor cage, an increase in Rra by a factor of 1.328 (i.e., α = 1.328
above and R’ra = 1.328·Rra) would mean that the resistance of one rotor bar had increased by a factor
of 83 (k = 83), if the other bars were not damaged. If there are m contiguous broken bars and two bars
next to them with the same damage k, then the R’ra/Rra relationship would be:
Sensors 2011, 11
3365
()
() ()
()
kmn
nk
R
R
n
R
R
R
k
m
n
k
Rk
m
n
R
Rk
m
n
R
RR
ra
ra
rai
ra
rai
rairai
rairai
rara
+
==
=
+
=
+
==
636
3
23
2
2
23
2
23
'
'
α
α
(17)
Furthermore, resistance exchange would be an inductance variation happening on misalignment,
rather than a mutual inductance variation appearing as variations in the self phase inductance L, due to
the variation in the number of rotor bars and a variation in mutual inductance, M (between the rotor
and stator) due to the reluctance exchange. The variations on R, L and M would pulse at rotor relative
speeds, and referring to stator rotating flux, this pulsation is s
fs , giving:
In the case of rotor resistance:
(
)
=
+=
=
3
2
2cos
3
2
2cos
2cos
3
2
1
π
παα
π
παα
παα
s
s
s
fs
fs
fs
(18)
In the case of rotor self-inductance:
(
)
=
+=
=
3
2
2cos
3
2
2cos
2cos
3
2
1
π
πκκ
π
πκκ
πκκ
s
s
s
fs
fs
fs
(19)
For the
κ
version, an equivalent equation can be used as given for α in the rotor resistance case,
depending on the number of rotor bars n, and degree of damage on rotor bars k:
()
kn
nk
L
L
ra
ra
+
== 33
'
κ
(20)
Mutual inductance must fulfill the same expression (14) as in the case of eccentricity.
These variations will give the equation substitutions on fundamental motor equations, which for the
case of broken rotor bars gives:
[] ()()()
+
+
+
=
3
2
1
100
010
001
α
α
α
rc
rb
ra
r
R
R
R
R (21)
Sensors 2011, 11
3366
()()()
+
+
+
3
2
1
1
1
1
κ
κ
κ
SBRR
RRBR
RRRA
RSRSRS
RSRSRS
RSRSRS
SRSRSR
SRSRSR
SRSRSR
SBSS
SSBS
SSSA
LMM
MLM
MML
MMM
MMM
MMM
MMM
MMM
MMM
LMM
MLM
MML
CBCA
BCBA
ACAB
CCCBCA
BCBBBA
ACABAA
CCCBCA
BCBBBA
ACABAA
CBCA
BCBA
ACAB
(22)
3.3. Simulink Motor Model Implementation
The parametric equation system just presented has been implemented on Simulink, with the
different blocks containing differential equations for stator and rotor phases, torque and differential
speed equations. In the differential equations for stator and rotor phases variable parameters have been
introduced, which represent the fault condition. Three additional blocks have been added to the main
model developed in Section 3 to introduce the additional frequency on the stator supply. The following
Figure 4 shows the expected harmonic composition on stator currents due to the injection, and the
appearance of the faulty harmonic at the frequency test and the additional composed harmonics.
Figure 4. Implemented injections on the parametric model.
The composed frequencies appear only in the case of motor misalignment, increasing in amplitude
with the increment of the fault condition. Figure 5 shows the expected harmonic distribution.
Sensors 2011, 11
3367
Figure 5. Injection of 125 Hz with no load; injected and composed harmonic distribution.
Figures 6, 7 and 8 show how harmonics appear due to the fault condition around the injected and
composed harmonics.
Figure 6. Detail of 175 Hz for 125 Hz injected frequency with low torque. This shows a
BRB fault condition.
Sensors 2011, 11
3368
Figure 7. 125 Hz Injected frequency test, low load.
Figure 8. Injection of 125 Hz, low load. Detail.
Figures 9 and 10 show a comparison between different composite frequencies; composite
frequencies appear only in the case of a fault condition, which implies a good fault-estimation
parameter for a motor operating with no load.
Sensors 2011, 11
3369
Figure 9. Composite frequencies 2 Fs + Fi.
Figure 10. Composite frequencies Fs + 2 Fi.
Low-frequency composed harmonics cause torque oscillations, which are confusing for simulation
results. Figure 11 shows frequency-composed harmonics at low frequencies, lower than the frequency
supply. The variation in amplitude in some harmonics can be appreciated, due to the fault condition
and torque oscillations during startup. These harmonics may hence be used to get good results in fault
detection.
Sensors 2011, 11
3370
Figure 11. Composed frequencies 2 Fs–Fi.
3.4. Influence of Injected Currents
To consider the effect of saturation on the rotor sheet, the induced field has been simulated by means
of FEM software. Different injected frequency tests will produce different effects on the motor; several
papers [8] introduce us to the injection theories for sensorless control motors. These references talk about
the motor as a band-pass. In order to ensure this, it is possible to simulate the flux density of current and
field on the stator and squirrel cage, using a simulator properly, introducing rotor and stator design and
introducing the frequency test found in Figure 12 (current flow density for 50 Hz frequency) and in
Figure 13 (current flow density for 200 Hz frequency) for the same voltage amplitude.
Figure 12. Flux density for 50 Hz frequency.
Sensors 2011, 11
3371
Figure 13. Flux density for 200 Hz frequency.
Having a look at the last two figures we can see that for the 200 Hz frequency test there is a bigger
current density, which confirms the idea that the motor could be considered as a band-pass with
200 Hz of central frequency of the band. In order to do this, we will try to inject our frequency test as
close as possible to 200 Hz.
Regarding the effect of saturation, the FEM analysis shows the flux distribution on the motor sheet
to be similar for the injected frequencies under analysis. Therefore, injecting a low current of
frequency test does not produce saturation on the motor sheet.
4. Experimental Procedure
4.1. Test Rig Experimental Setup
A three-phase, 1.1 kW, 380 V and 2.6 A, 50 Hz, 1,410 rpm, four-pole induction motor was used in
this study. First of all, its healthy performance was analyzed and, afterwards, one-sixth of the rotor
bars were damaged. The current has been measured by an A622 Tektronix current probe, 100 Ampere
AC/DC. The current ranges are 0/100 mV/A, and the typical DC accuracy is ±3% ± 50 mA at 100
mV/A (50 mA to a 10 A peak). The frequency range goes from DC to 100 kHz (3 dB).
4.2. Signal Acquisition Requirements
Auxiliary test voltage was injected at frequencies of 80 Hz, 125.5 Hz, 176 Hz, and 200 Hz, and
amplitudes of 29 V, 36 V, 43 V, and 46.5 V, respectively. To inject the test frequency, different
options have been tested, including the use of a synchronous machine to achieve a complete sinusoidal
auxiliary supply. At present, an AC frequency inverter is used which is able to inject an auxiliary test
voltage from 0 Hz to 400 Hz and from 0 to 250 VAC.
Frequency sidebands were checked around some of the new current harmonics obtained in (10),
especially:
isc fff += 2
1, isc fff
2
2, isc fff 2
3
+
=
Sensors 2011, 11
3372
where fci is the composed frequency (Table 1). New fault harmonics are expected at frequencies
provided by (10).
Table 1. Injected and Composed Frequencies.
Supply Frequency (f
s
) = 50 Hz
Injected Frequency (fi) fc1 = -2f
s
+ fi f
c2 = 2f
s
+ fi f
c3 = f
s
+ 2fi
Hz Hz Hz Hz
79.9 20.1 179.9 209.8
125.5 25.5 225.5 301
175.8 75.8 275.8 401.6
200 100 300 450
Several tests have been carried out taking the aforementioned into account. These validate the idea
of using an auxiliary voltage test signal and analyzing the sideband harmonics for the detection of a
faulty induction motor.
The load was adjusted by means of a DC motor working as a generator and by supplying a set of
resistors. The motor was supplied with 220 VAC, star connection. This means 150 V AC per phase,
which leads to a speed lower than the nominal (1,275 rpm), and a slip frequency higher than the
nominal value (approximately 15%). Using this connection does not affect the main conclusions of the
paper, although the results are shown in a much clearer manner.
Figures 14 and 15 show the standard MCSA spectrum around the main frequency of 50 Hz, both for
a healthy and for a faulty motor, and for each frequency injected. The rotor was running at 1,275 rpm,
and the faulty frequencies for broken rotor bars are shown at 15 Hz from the generating frequency,
approximately (Figure 14). The ratio between the harmonic due to the fault and the main harmonic is
lower than 1%. This result agrees with that expected from applying the classical MCSA method.
Figure 14. Stator current spectrum for a healthy motor with a load.
30 35 40 45 50 55 60 65 70
0
0.005
0.01
0.015
0.02
0.025
0.03
Frequency (Hz)
Amplitude (A)
Fi=79.9 Hz
Fi=125.5 Hz
Fi=175.8 Hz
Fi=200.1 Hz
2.5 A
Sensors 2011, 11
3373
Figure 15. Stator current spectrum for a faulty motor with a load.
The current spectra around fc1, fc2, and fc3 for every frequency injected, for a healthy motor, are
shown in Figures 16, 17 and 18. To show the effects of every frequency better, composition
frequencies were centered at 0 Hz and the resulting faulty frequencies were located around this central
position.
As expected, frequency compositions fc1 have higher amplitude than fc2 and fc3 in a healthy
motor, because they are at a greater distance from the pole of the low-pass motor filter.
Figure 16. Stator current spectrum around fc1 for a healthy motor.
30 35 40 45 50 55 60 65 70
0
0.005
0.01
0.015
0.02
0.025
0.03
Fre
q
uenc
y
(
Hz
)
Amplitude (A)
Fi=79.9 Hz
Fi=125.5 Hz
Fi=175.8 Hz
Fi=200.1 Hz
2.5 A
-20 -15 -10 -5 0 5 10 15 20
0
0.0025
0.005
0.0075
0.01
0.0125
0.015 Amplitude Spectrum: -2*fs + fi
Frequency (Hz)
Amplitude (A)
Fi=79.9 Hz
Fi=125.5 Hz
Fi=175.8 Hz
Fi=200.1 Hz
Sensors 2011, 11
3374
Figure 17. Stator current spectrum around fc2 for a healthy motor.
Figure 18. Stator current spectrum around fc3 for a healthy motor.
Figures 19, 20 and 21 show the current spectrum around fc1, fc2, and fc3 for every frequency
injected to a faulty motor. As expected, the corresponding current spectrum component due to the fault
is 15 Hz in every figure. However, the spectrum around fc1 has plenty of different harmonics, which
makes it difficult to identify the fault. This is because the centered frequencies are 25.5 Hz, 76 Hz and
100 Hz, and the sidebands are in the range of 5 Hz to 120 Hz. It is in this range that we can locate most
harmonics in a real machine: rotor eccentricities, flux unbalances, and mechanical shocks, among
others. On the other hand, Figure 20 and Figure 21 show much clearer spectra, although the amplitudes
of the harmonics are lower around fc3 because they are close to the cut-off frequency of the low-pass
motor filter.
-20 -15 -10 -5 0 5 10 15 20
0
1
2
3
4
5
6
7x 10
-3
A
mplitude Spectrum: 2*fs + fi
Frequency (Hz)
Amplitude (A)
Fi=79.9 Hz
Fi=125.5 Hz
Fi=175.8 Hz
Fi=200.1 Hz
-20 -15 -10 -5 0 5 10 15 20
0
1
2
3
4
5
6
7x 10-3 Amplitude Spectrum: fs + 2*fi
Fre
q
uenc
y
(
Hz
)
Amplitude (A)
Fi=79.9 Hz
Fi=125.5 Hz
Fi=175.8 Hz
Fi=200.1 Hz
Sensors 2011, 11
3375
Figure 19. Stator current spectrum around fc1 for a faulty motor.
Figure 20. Stator current spectrum around fc2 for a faulty motor.
Figure 21. Stator current spectrum around fc3 for a faulty motor.
-20 -15 -10 -5 0 5 10 15 20
0
1
2
3
4
5x 10
-3
Amplitude Spectrum: -2*fs + fi
Frequency (Hz)
Amplitude (A)
Fi=79.9 Hz
Fi=125.5 Hz
Fi=175.8 Hz
Fi=200.1 Hz
25 mA
-20 -15 -10 -5 0 5 10 15 20
0
1
2
3
4
5
6
7x 10-3
A
mplitude Spectrum: 2*fs + fi
Frequency (Hz)
Amplitude (A)
Fi=79.9 Hz
Fi=125.5 Hz
Fi=175.8 Hz
Fi=200.1 Hz
-20 -15 -10 -5 0 5 10 15 20
0
1
2
3
4
5
6
7x 10
-3
Amplitude Spectrum: fs + 2*fi
Frequency (Hz)
Amplitude (A)
Fi=79.9 Hz
Fi=125.5 Hz
Fi=175.8 Hz
Fi=200.1 Hz
Sensors 2011, 11
3376
Although the amplitude of these new fault components is quite reduced, the 10% ratio found
between the fault frequency and the generating frequency is higher than the 1% ratio calculated for the
standard components used in the classical MCSA (Figure 12).
Generating frequencies in Figure 19 are of the same order as the main frequency. This means that
the test signals affect the motor’s operation, to then change the slip. This fact, combined with the
unclear spectrum, makes low-frequency compositions fc1 unsuitable for the detection of rotor faults.
Figure 20 and Figure 21 show faulty frequencies exactly with the expected values. However, the
generating frequencies are too large in the case of fc3 and the resulting harmonics are too small and
difficult to measure and analyze. On the contrary, Figure 20 shows not only an excellent relationship
between generating and resulting frequencies of about 11%, but also a fault harmonic amplitude of
2 e-3A, which is enough to be obtained and analyzed. Therefore, the proposed method consists of
capturing and analyzing these new current spectral components that appear due to the signal
composition between main and injected frequencies.
Some relatively important harmonics appear in the spectra for both healthy and faulty machines.
For instance, Figure 17 and Figure 20 show a 10 Hz frequency component of 1.5e-3 A for Fi = 80 Hz,
which corresponds to 170 Hz in the stator current spectrum. This component, which is not directly
related to the fault, is due to the frequency composition (5Fs–Fi). A similar explanation can be offered
for the +10 Hz frequency component of 2e-3 A in Figures 18 and 21, which is due to the frequency
composition (3Fs–Fi). In this case, the real stator component is 220 Hz. Obviously, all these
frequencies which are due to frequency compositions given by (10) should not be considered for fault
analysis.
The amplitude of the compound frequencies fci in the stator current spectrum is shown in Figure 22.
From the figure, it can be concluded that the magnitude of fc1 in a healthy motor is larger than in a
faulty motor. However, the magnitude of fc2 and fc3 in a healthy motor is smaller than for a faulty
motor.
These conclusions are applicable to every frequency injected. Thus, specific compositions fc2 and
fc3 could also be used to detect rotor failures, because their amplitude, for every frequency injected, is
clearly higher in the damaged motor.
To detect a fault, the sideband around the expected fault frequency is monitored for a period of time
after applying a test frequency. The diagnostic system will look for a specific harmonic amplitude
increase. If it appears, and the relationship between the generating frequency fc2 and the fault
frequency is higher than a predetermined value, then the fault will be detected. Compared with the
standard MCSA method, the only drawback is that it is necessary to generate and apply the test signal
to the stator phases. However, the generation of a 75–200 Hz sine wave is not a problem for the
modulator included in every present frequency inverter. On the other hand, the measurement of the
current phases is already used in the MCSA method, as well as for control purposes.
The selection of the test signal frequency is a trade-off between several concerns. The carrier
frequency must be high enough to create a deep bar effect that prevents the high frequency flux wave
from substantially linking to the rotor bars, but it must also be low enough so that the skin effect in the
rotor laminations does not repel the flux from penetrating below the rotor surface.
Sensors 2011, 11
3377
In a practical case, a low-pass filter model of the machine can be proposed, with the pole frequency
in 400 Hz. Therefore, the interaction between main and signal test frequencies should cause new
harmonic components lower than this value in order to get good results.
Figure 22. Amplitude of the stator composed frequencies.
In case of incipient fault condition the appearance of fault harmonics and composed harmonics
remains. However, the amplitude of harmonics is directly related with the fault condition. other testing
has also been carried out with inverter supply and low fault condition 1 and 2 BRB. In the following it
is shown and the main testing results are discussed
Main Supply, Vphase = 230 Vrms f = 50 Hz
Test voltage, Vphase =20 Vrms f1 = 80 Hz, f2 = 125 Hz;
Figure 23 shows the fault condition and the compositions off signals over the spectrum.
Figure 23. Band Current Spectrum for 1 BRB motor.
Frequency (Hz)
4.3. Mention for VVVF Converter Supply
Although the injected voltage was obtained from an auxiliary generator through a serial
transformer, there is no problem to generate a composed three-phase sine wave with the desired test
frequency by using a special modulation reference in the Space Vector Modulation block of the power
fi - 2*fs 2*fs + fi fs + 2*fi
0
0.002
0.004
0.006
0.008
0.01
Amplitude (A)
Fi=79.9 Hz MHealthy
Fi=125.5 Hz MHealthy
Fi=175.8 Hz MHealthy
Fi=200.1 Hz MHealthy
Fi=79.9 Hz MFault
Fi=125.5 Hz MFault
Fi=175.8 Hz MFault
Fi=200.1 Hz MFault
050 100 150 200 250 300
9
0
8
0
70
6
0
5
0
4
0
3
0
2
0
10
0
Amplitude (db)
Sensors 2011, 11
3378
inverter. For a practical implementation in industrial equipment, the frequency test signal should be
higher than the bandwidth of the current loop, especially when vectorial control is applied to IM. In
that case, the choice of frequency test signal will be the same as in sinusoidal application, more or less
on the 80–200 Hz band. In order to allow subharmonics due to the modulation we introduce a
reactance high-pass filter between the drive and the VVVF converter, which cuts subharmonics due to
an asynchronous modulation. Figure 24 shows the amplitude comparison between composed
harmonics for 1 Broken Rotor Bar, 2 and 4. The injected frequencies chosen have been the most
promising ones for fault detection (80 Hz and 125 Hz).
Figure 24. Amplitude comparison of the stator composed frequencies for different fault condition.
5. Conclusions
Signal injection ensures proper results in the detection of faults, especially in cases of low torque.
The use of an anti-clockwise injected frequency introduces additional slip on the motor which allows
the detection of faults with a better dynamic resolution. Furthermore, the composed frequencies are
good indicators of the behavior of machine faults. It has been clearly demonstrated that in the case of a
fault condition some of these composed frequencies increase their values, which implies unbalances in
the machine that could be understood as a fault condition.
However, the composed frequencies only introduce the notion of unbalances, but they cannot
differentiate between rotor misalignments and BRB fault conditions, in order to get a proper diagnosis.
The fault condition could be distinguished by analyzing the current spectral distribution about injected
and composed harmonics, but the location of faulty harmonics depends on the slip value, which means
that in case of a variable load the fault condition cannot be clearly appreciated.
In conclusion, it is possible to establish that:
The signal injection technique is a good method for fault detection under low load, through
examination of the fault harmonics on the injected signal and the frequency compositions.
The signal injection technique is a good estimator of conditions of unbalance, through
examination of the amplitude of the composed frequency.
0
0,001
0,002
0,003
0,004
0,005
0,006
0,007
0,008
fi2*fs 2*fs+fi fs+2*fi
80HzHealthy
80Hz1BRB
80Hz2BRB
80Hz4BRB
125HzHealthy
125Hz1BRB
125Hz2BRB
125Hz4BRB
Amplitude (A)
Sensors 2011, 11
3379
In case of a variable load, the composed frequency should ensure unbalance, but
improvements will be needed in the field of signal processing to distinguish fault
conditions.
Acknowledgements
The authors wish to acknowledge the financial support received from the Ministerio de Ciencia y
Tecnología de España (Spanish Ministry of Science and Technology) for carrying out this work, under
the TRA2010-21598-C02-01 Research Project.
References
1. Cabanas, M.F.; Melero, M.G.; Orcajo, G.A.; Cano, J.M.; Solares, J. Técnicas para el
Mantenimiento y diagnóstico de Máquinas Eléctricas Rotativas. Marcombo: Oviedo, Spain, 1996.
2. Meador, D. Tools for O&M, from Building Controls to Thermal Imaging. In Proceedings of
O&M Workshop for Government Facility Managers, Washington, DC, USA, 19 June 2003.
3. Vas, P. Parameter Estimation, Condition Monitoring, and Diagnosis of Electrical Machines;
Oxford University Press: New York, NY, USA, 1993.
4. Zidat, F.; Lecointe, J.-P.; Morganti, F.; Brudny, J.-F.; Jacq, T.; Streiff, F. Non Invasive Sensors
for Monitoring the Efficiency of AC Electrical Rotating Machines. Sensors 2010, 10, 7874-7895.
5. Wang, H.Q.; Chen, P. A Feature Extraction Method Based on Information Theory for Fault
Diagnosis of Reciprocating Machinery. Sensors 2009, 9, 2415-2436.
6. Gao, L.X.; Ren, Z.Q.; Tang, W.L.; Wang, H.Q.; Chen, P. Intelligent Gearbox Diagnosis Methods
Based on SVM, Wavelet Lifting and RBR. Sensors 2010, 10, 4602-4621.
7. Nandi, S.; Toliyat, H.A. Condition Monitoring and Fault Diagnosis of Electrical
Machines-A Review. IEEE Trans. Energy Convers. 1999, 10, 1906-1915.
8. Nandi, S.; Toliyat, H.A.; Li, X.D. Condition Monitoring and Fault diagnosis of Electrical
Motors- A Review. IEEE Trans. Energy Convers. 2005, 20, 719-729.
9. Benbouzid, M.E.H.; Vieira, M.; Theys, C. Induction Motor Faults Detection and Location Using
Stator Current Advanced Signal Processing Techniques. IEEE Trans. Power Elect. 1999, 14,
14-22.
10. Benbouzid, M.E.H.; Kliman, G.B. What Stator Current Processing-Based Technique to Use for
Induction Motor Rotor Fault Diagnosis? IEEE Trans. Energy Convers. 2003, 18, 238-244.
11. Thomson, W.T.; Fenger, M. Current Signature Analysis to Detect Induction Motor Faults. IEEE
Trans. Ind. Appl. Mag. 2001, 15, 26-34.
12. Thomson, W.T.; Morrrison, D. On-line Diagnosis of Stator Shorted Turns in Mains and Inverter
Fed Low Voltage Induction Motors. In Proceedings of IEEE Power Electronics Machines and
Drives Conference, Bath, UK, 16–18 April 2002; pp. 122-127.
13. Schoen, R.R.; Habetler, T.G.; Kamran, F.; Bartheld, R.G. Motor Bearing Damage Detection
Using Stator Current Monitoring. IEEE Trans. Ind. Appl. 1994, 26, 114-116.
14. Korde, A. On-line Condition Monitoring of Motors Using Electrical Signature Analysis, Recent
Advances in Condition-Based Plant Maintenance. In Seminar Organized by Indian Institute of
Plant Engineers, Mumbai, India, 17–18 May 2002.
Sensors 2011, 11
3380
15. Miletic, A.; Cettolo, M. Frequency Converter Influence on Induction Motors Rotor Faults
Detection Using Motor Current Signature Analysis Experimental Research. In Proceedings of
Symposium on Diagnostic for Electrical Machines, Power Electronics and Drives, SDEMPED,
Atlanta, GA, USA, 24–26 August 2003; pp. 124-128.
16. Alford, T. Motor Current Analysis and its Applications in Induction Motors Fault Diagnosis;
ENTEK IRD, International Corporation: Milford, OH, USA, 1999; pp. 1-24.
17. Haylock, A.; Mecrow, B.C.; Jack, A.G; Atkinson, D.J. On-line Detection of Winding
Short-Circuit in Inverter Fed Drives. In Proceedings of Ninth International Conference on
Electrical Machines and Drives, Canterbury, UK, 1–3 September 1999; pp. 258-262.
18. Welchko, B.A.; Jahns, T.M.; Hiti, S. IPM Synchronous Machine Drive Response to a
Single-Phase Open Circuit Fault. IEEE Trans. Power Electro. 2002, 17, 764-771.
19. Bellini, A.; Filippetti, F.; Franceschini, G.; Tassoni, C.; Passaglia, R.; Saottini, M.; Tontini, G.;
Giovannini, M.; Rossi, A. On-Field Experience with Online Diagnosis of Large Induction Motors
Cage Failures Using MCSA. IEEE Trans. Ind. Appl. 2002, 38, 1045-1053.
20. Henao, H.; Capolino, G.A.; Razik, H. Analytical Approach of the Stator Current Frequency
Harmonics Computation for Detection of Induction Machine Rotor Faults. In Proceedings of
Symposium on Diagnostic for Electrical Machines, Power Electronics and Drives, SDEMPED,
Atlanta, GA, USA, 24–26 August 2003; pp. 259-264.
21. Cabanas, M.F.; Pedrayes, F.; Ruiz, M.; Melero, M.G.; Orcajo, G.A.; Cano, J.M.; Rojas, C.H. A
new On-Line Method for the Early Detection of Broken Rotor Bars in Asynchronous Motors
Working under Arbitrary Load Conditions. In Proceedings of IEEE Industrial Applications
Symposium, IAS 2005, Hong Kong, China, 2–5 October 2005; pp. 662-669.
22. Ballal, S.; Khan, Z.J.; Suryawanshi, H.M.; Sonolikar, R.L. Adaptive Neural Fuzzy Inference
System for the Detection of Inter-Turn Insulation and Bearing Wear Faults in Induction Motor.
IEEE Trans. Ind. Electron. 2007, 54, 189-199.
23. Ho, S.L.; Cheng, K.W.E. Condition Monitoring of Rotor Faults in Induction Motors by Injection
of Low Frequency Signals into the Supply. In Proceedings of Power Electronics and Variable
Speed Drives, Sorrento, Italy, 21–23 September 1998; pp. 200-205.
24. Demian, C.; Mpanda-Mabwe, A.; Henao, H.; Capolino, G.A. Detection of Induction Machines
Rotor Faults at Standstill Using Signals Injection. IEEE Trans. Ind. Appl. 2004, 40, 1550-1559.
25. Briz, F.; Degner, M.W.; Zamarrón, A.; Guerrero, J.M. Online StatorWinding Fault Diagnosis in
Inverter-Fed AC Machines Using High-Frequency Signal Injection. IEEE Trans. Ind. Appl. 2003,
39, 1109-1117.
26. Briz, F.; Degner, M.W.; Zamarrón, A.; Guerrero, J.M. Online Diagnostics in Inverter-Fed AC
Machines Using High-Frequency Signal Injection. IEEE Trans. Ind. Appl. 2004, 40, 1109-1117.
© 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution license
(http://creativecommons.org/licenses/by/3.0/).
... The most commonly used diagnosis method is the motor current signature analysis (MCSA), which is based on spectrum analysis of the stator current in the steady state. This can be successfully combined with other stator-current-based methods used to diagnose the rotor cage faults [16][17][18][19][20], rotor eccentricity [17,[21][22][23], bearing faults [17,22,24] and stator winding short circuits [25,26]. The basic advantage of this method is that it is very easy to perform stator current measurements. ...
... The most commonly used diagnosis method is the motor current signature analysis (MCSA), which is based on spectrum analysis of the stator current in the steady state. This can be successfully combined with other stator-current-based methods used to diagnose the rotor cage faults [16][17][18][19][20], rotor eccentricity [17,[21][22][23], bearing faults [17,22,24] and stator winding short circuits [25,26]. The basic advantage of this method is that it is very easy to perform stator current measurements. ...
... s can be subjected to further analysis, as was done in [1,9,10]. Equation (22) might then be defined as ...
Article
Full-text available
This paper presents some considerations regarding the application of the stator zero-sequence current component (ZSC) in the fault detection of cage induction machines, including the effects of magnetic core saturation. Faults such as rotor cage asymmetry and static, dynamic, and mixed eccentricity were considered. The research started by developing a harmonic motor model, which allowed us to obtain a voltage equation for the zero-sequence current component. The equation allowed us to extract formulas of typical frequencies for particular fault types. Next, in order to verify the effectiveness of ZSC in induction motor fault diagnosis, finite element calculations and laboratory tests were carried out for the previously mentioned faults for delta and wye connections with neutral wire stator winding configurations.
... Most of the work in the area of process systems engineering for FDD is based on passive approaches where the system outputs are monitored for detecting observable statistical changes. The active approach for FDD involves injecting persistently exciting input signal of specific bandwidth into the system and using the resulting input-output data for incipient fault detection and diagnosis [19,13,6]. In this work, a blend of both passive and active approaches are used where the passive approach is shown to be effective for identifying most faults but an active approach is required for detecting incipient faults. ...
Preprint
A Deep Neural Network (DNN) based algorithm is proposed for the detection and classification of faults in industrial plants. The proposed algorithm has the ability to classify faults, especially incipient faults that are difficult to detect and diagnose with traditional threshold based statistical methods or by conventional Artificial Neural Networks (ANNs). The algorithm is based on a Supervised Deep Recurrent Autoencoder Neural Network (Supervised DRAE-NN) that uses dynamic information of the process along the time horizon. Based on this network a hierarchical structure is formulated by grouping faults based on their similarity into subsets of faults for detection and diagnosis. Further, an external pseudo-random binary signal (PRBS) is designed and injected into the system to identify incipient faults. The hierarchical structure based strategy improves the detection and classification accuracy significantly for both incipient and non-incipient faults. The proposed approach is tested on the benchmark Tennessee Eastman Process resulting in significant improvements in classification as compared to both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies.
... Active FDD involves injecting persistently exciting input signals into the system and using the resulting input-output data for incipient fault detection and diagnosis. Heirung and Mesbah [2019], Cusidó et al. [2011], Busch and Peddle [2014]. The disadvantage of active FDD is that it introduces an external disturbance to the process which may temporarily impact the operation and thus its use should be limited. ...
Thesis
Full-text available
The last decade has seen remarkable advances in speech, image, and language recognition tools that have been made available to the public through computer and mobile devices’ applications. Most of these significant improvements were achieved by Artificial Intelligence (AI)/ deep learning (DL) algorithms (Hinton et al., 2006) that generally refers to a set of novel neural network architectures and algorithms such as long-short term memory (LSTM) units, convolutional networks (CNN), autoencoders (AE), t-distributed stochastic embedding (TSNE), etc. Although neural networks are not new, due to a combination of relatively novel improvements in methods for training the networks and the availability of increasingly powerful computers, one can now model much more complex nonlinear dynamic behaviour by using complex structures of neurons, i.e. more layers of neurons, than ever before (Goodfellow et al., 2016). However, it is recognized that the training of neural nets of such complex structures requires a vast amount of data. In this sense manufacturing processes are good candidates for deep learning applications since they utilize computers and information systems for monitoring and control thus generating a massive amount of data. This is especially true in pharmaceutical companies such as Sanofi Pasteur, the industrial collaborator for the current study, where large data sets are routinely stored for monitoring and regulatory purposes. Although novel DL algorithms have been applied with great success in image analysis, speech recognition, and language translation, their applications to chemical processes and pharmaceutical processes, in particular, are scarce. The current work deals with the investigation of deep learning in process systems engineering for three main areas of application: (i) Developing a deep learning classification model for profit-based operating regions. (ii) Developing both supervised and unsupervised process monitoring algorithms. (iii) Observability Analysis It is recognized that most empirical or black-box models, including DL models, have good generalization capabilities but are difficult to interpret. For example, using these methods it is difficult to understand how a particular decision is made, which input variable/feature is greatly influencing the decision made by the DL models etc. This understanding is expected to shed light on why biased results can be obtained or why a wrong class is predicted with a higher probability in classification problems. Hence, a key goal of the current work is on deriving process insights from DL models. To this end, the work proposes both supervised and unsupervised learning approaches to identify regions of process inputs that result in corresponding regions, i.e. ranges of values, of process profit. Furthermore, it will be shown that the ability to better interpret the model by identifying inputs that are most informative can be used to reduce over-fitting. To this end, a neural network (NN) pruning algorithm is developed that provides important physical insights on the system regarding the inputs that have positive and negative effect on profit function and to detect significant changes in process phenomenon. It is shown that pruning of input variables significantly reduces the number of parameters to be estimated and improves the classification test accuracy for both case studies: the Tennessee Eastman Process (TEP) and an industrial vaccine manufacturing process. The ability to store a large amount of data has permitted the use of deep learning (DL) and optimization algorithms for the process industries. In order to meet high levels of product quality, efficiency, and reliability, a process monitoring system is needed. The two aspects of Statistical Process Control (SPC) are fault detection and diagnosis (FDD). Many multivariate statistical methods like PCA and PLS and their dynamic variants have been extensively used for FD. However, the inherent non-linearities in the process pose challenges while using these linear models. Numerous deep learning FDD approaches have also been developed in the literature. However, the contribution plots for identifying the root cause of the fault have not been derived from Deep Neural Networks (DNNs). To this end, the supervised fault detection problem in the current work is formulated as a binary classification problem while the supervised fault diagnosis problem is formulated as a multi-class classification problem to identify the type of fault. Then, the application of the concept of explainability of DNNs is explored with its particular application in FDD problem. The developed methodology is demonstrated on TEP with non-incipient faults. Incipient faults are faulty conditions where signal to noise ratio is small and have not been widely studied in the literature. To address the same, a hierarchical dynamic deep learning algorithm is developed specifically to address the issue of fault detection and diagnosis of incipient faults. One of the major drawbacks of both the methods described above is the availability of labeled data i.e. normal operation and faulty operation data. From an industrial point of view, most data in an industrial setting, especially for biochemical processes, is obtained during normal operation and faulty data may not be available or may be insufficient. Hence, we also develop an unsupervised DL approach for process monitoring. It involves a novel objective function and a NN architecture that is tailored to detect the faults effectively. The idea is to learn the distribution of normal operation data to differentiate among the fault conditions. In order to demonstrate the advantages of the proposed methodology for fault detection, systematic comparisons are conducted with Multiway Principal Component Analysis (MPCA) and Multiway Partial Least Squares (MPLS) on an industrial scale Penicillin Simulator. Past investigations reported that the variability in productivity in the Sanofi's Pertussis Vaccine Manufacturing process may be highly correlated to biological phenomena, i.e. oxidative stresses, that are not routinely monitored by the company. While the company monitors and stores a large amount of fermentation data it may not be sufficiently informative about the underlying phenomena affecting the level of productivity. Furthermore, since the addition of new sensors in pharmaceutical processes requires extensive and expensive validation and certification procedures, it is very important to assess the potential ability of a sensor to observe relevant phenomena before its actual adoption in the manufacturing environment. This motivates the study of the observability of the phenomena from available data. An algorithm is proposed to check the observability for the classification task from the observed data (measurements). The proposed methodology makes use of a Supervised AE to reduce the dimensionality of the inputs. Thereafter, a criterion on the distance between the samples is used to calculate the percentage of overlap between the defined classes. The proposed algorithm is tested on the benchmark Tennessee Eastman process and then applied to the industrial vaccine manufacturing process.
... This method is mainly applied to detect speeddependent fault harmonics in the frequency spectrum of stator current [18], although other magnitudes can also be used (e.g., instantaneous power, reactive power or apparent power [19][20][21]). Many research papers, whose main purpose is not localizing the fault harmonic, since they test lab motors with perfectly-known conditions, analyze the expected fault harmonic frequency band assuming that the highest peak of the band will be the fault harmonic [22][23][24][25][26][27][28][29][30]. Some other authors use filters as wavelet transform to extract sub-signals related to frequency bands where the harmonic is supposed to be [31][32][33]. ...
Article
Full-text available
Sensorless speed estimation has been extensively studied for its use in control schemes. Nevertheless, it is also a key step when applying Motor Current Signature Analysis to induction motor diagnosis: accurate speed estimation is vital to locate fault harmonics, and prevent false positives and false negatives, as shown at the beginning of the paper through a real industrial case. Unfortunately, existing sensorless speed estimation techniques either do not provide enough precision for this purpose or have limited applicability. Currently, this is preventing Industry 4.0 from having a precise and automatic system to monitor the motor condition. Despite its importance, there is no research published reviewing this topic. To fill this gap, this paper investigates, from both theoretical background and an industrial application perspective, the reasons behind these problems. Therefore, the families of sensorless speed estimation techniques, mainly conceived for sensorless control, are here reviewed and thoroughly analyzed from the perspective of their use for diagnosis. Moreover, the algorithms implemented in the two leading commercial diagnostic devices are analyzed using real examples from a database of industrial measurements belonging to 79 induction motors. The analysis and discussion through the paper are synthesized to summarize the lacks and weaknesses of the industry application of these methods, which helps to highlight the open problems, challenges and research prospects, showing the direction in which research efforts have to be made to solve this important problem.
... The most frequently used signal for diagnosing rotor eccentricity is the stator current [8,[11][12][13][14][15]. This method consists of finding harmonics in the current spectrum with frequencies characteristic for a given type of eccentricity and then analyzing their amplitudes. ...
Article
Full-text available
In the condition monitoring of induction machines operating in various industry sectors, the assessment of eccentricity is as important as the assessment of the condition of windings, bearings, mechanical vibrations or noise. The reasons for the eccentricity can be various; for example, rotor imbalance, damage or wear of the bearings, improper alignment of the rotor and the load machine and finally, assembly errors after overhaul. Disregard of this phenomenon during routine tests may result in the development of vibrations transmitted to the stator windings, faster wear of the bearings and even, in extreme cases, rubbing of the rotor against the stator surface and damage to the windings and local overheating of the machine core. On the basis of years of experience in the diagnosis of large induction machines operating in various industries, the article deals with the problem of developing reliable indicators for assessing the levels of commonly accepted types of eccentricity. Starting from field calculations and analyzing various cases of eccentricity, the methodology for determining the indicators for evaluation from the stator current spectrum is shown. The changes in the values of these indices for various cases of simultaneous occurrence of static and dynamic eccentricity are shown. The calculation results were verified in the laboratory. Also shown are three interesting cases from diagnostic practice in the evaluation of high-power machines in the industry. It has been shown that the proposed indicators are useful and enable an accurate diagnosis of levels of eccentricity.
... Most of the work in the area of process systems engineering for FDD is based on passive approaches where the system outputs are monitored for detecting observable statistical changes. The active approach for FDD involves injecting persistently exciting input signal of specific bandwidth into the system and using the resulting input-output data for incipient fault detection and diagnosis [19,13,6]. In this work, a blend of both passive and active approaches are used where the passive approach is shown to be effective for identifying most faults but an active approach is required for detecting incipient faults. ...
Preprint
Full-text available
A Deep Neural Network (DNN) based algorithm is proposed for the detection and classification of faults in industrial plants. The proposed algorithm has the ability to classify faults, especially incipient faults that are difficult to detect and diagnose with traditional threshold-based statistical methods or by conventional Artificial Neural Networks (ANNs). The algorithm is based on a Supervised Deep Recurrent Autoencoder Neural Network (Supervised DRAE-NN) that uses dynamic information of the process along the time horizon. Based on this network a hierarchical structure is formulated by grouping faults based on their similarity into subsets of faults for detection and diagnosis. Further, an external pseudo-random binary signal (PRBS) is designed and injected into the system to identify incipient faults. The hierarchical structure based strategy improves the detection and classification accuracy significantly for both incipient and non-incipient faults. The proposed approach is tested on the benchmark Tennessee Eastman Process resulting in significant improvements in classification as compared to both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies.
... Acoustic signal processing is a distinctive application in numerous sectors in life, like industry and communications. In electric motor diagnosis, acoustic noise investigation is a robust alternative to complement the information given by other methods [1][2][3][4]. A common issue in these machines is the occasional occurrence of rotor damage, such as broken bars. ...
Article
Full-text available
We apply power spectral analysis based on covariance function and spectral subtraction to detect adjacent and non-adjacent bar breakages. We obtain a spectral pattern when the signal presents one or various broken bars, independent of the relative position of the bar breakages. The proposed algorithm gives satisfactory results for detectability compared to some previous research. Additionally, we also present illustrations of faults and signal to noise in the noise-reduction stage.
Article
This proposed Rigorous Investigation of Stator Current (RISC) Envelope, provides a better way in pre-determining the healthy condition of an induction motor under the effect of stator faults. The stator faults analysed here are open circuit and short circuit faults. Stator current is captured and investigated to understand the condition of the motor. Rigorous analysis of the stator current is done using Hilbert Spectrum Analysis (HSA) method to understand the responses of induction motor under the influence of the stator faults. The performance of induction motor under faults are analysed, to predict the satisfactory operation of motor. Simulation is carried out in MATLAB platform. The investigated performance analysis of the induction motor exhibits the effective process of monitoring the healthiness of the induction motor.
Article
Full-text available
This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to.
Article
Full-text available
Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.
Article
Full-text available
This paper presents a non invasive method for estimating the energy efficiency of induction motors used in industrial applications. This method is innovative because it is only based on the measurement of the external field emitted by the motor. The paper describes the sensors used, how they should be placed around the machine in order to decouple the external field components generated by both the air gap flux and the winding end-windings. The study emphasizes the influence of the eddy currents flowing in the yoke frame on the sensor position. A method to estimate the torque from the external field use is proposed. The measurements are transmitted by a wireless module (Zig-Bee) and they are centralized and stored on a PC computer.
Conference Paper
Full-text available
Induction motors have nowadays been more frequently used with frequency converters. The use of frequency converters affects the behavior of the machine and makes fault diagnosis more complicated. One of the widely used diagnostic methods is motor current signature analysis (MCSA). This method is based on measurement of sidebands in the stator current spectrum. Those sidebands are usually located close to the main supply frequency. Frequency converter causes supply frequency to slightly vary in time and, as a result, some additional harmonics in the current spectrum are induced and sidebands are reduced. Those harmonics can be easily misinterpreted as the sidebands caused by the rotor faults. Although the rotor faults in the case of frequency converter supply are not so often, precaution is still necessary. In this paper the experimental results of fault diagnosis carried out using standard supply and using frequency converter have been compared and presented. All tests were performed on 22 kW, 380 V, 1470 r/min induction motor. The current spectra are given for the motor with two broken bars with both types of supply.
Article
In recent years, marked improvement has been achieved in the design and manufacture of stator winding. However, motors driven by solid-state inverters undergo severe voltage stresses due to rapid switch-on and switch-off of semiconductor switches. Also, induction motors are required to operate in highly corrosive and dusty environments. Requirements such as these have spurred the development of vastly improved insulation material and treatment processes. But cage rotor design has undergone little change. As a result, rotor failures now account for a larger percentage of total induction motor failures. Broken cage bars and bearing deterioration are now the main cause of rotor failures. Moreover, with advances in digital technology over the last years, adequate data processing capability is now available on cost-effective hardware platforms, to monitor motors for a variety of abnormalities on a real time basis in addition to the normal motor protection functions. Such multifunction monitors are now starting to displace the multiplicity of electromechanical devices commonly applied for many years. For such reasons, this paper is devoted to a comparison of signal processing-based techniques for the detection of broken bars and bearing deterioration in induction motors. Features of these techniques which are relevant to fault detection are presented. These features are then analyzed and compared to deduce the most appropriate technique for induction motor rotor fault detection.
Conference Paper
Electrical equipments are the workhorses of industry; their failure may result in complete shut down of a plant or even cause an unexpected disaster. Researchers had pursued rigorously various diagnostic approaches for electrical machines. Apart from analyzing the conventional vibration, current, voltage signals people are trying to explore fault signatures from torque, power, speed, flux etc. Methods such as offline/online, with/without additional sensor, model-based, signal-based etc. are being explored vastly. A number of signal processing techniques and fault detection decision-making tools are being reported frequently. Undoubtedly this field is vast in scope. Hence keeping this in mind to avoid repetition as well to facilitate future research a brief review is presented in this paper. Nearly 80% electrical motors used in industries are induction motors and hence industries depend on the performance of them to a great extend. This paper will mainly concentrate on induction machines with a very brief review of other machines.
Article
Contiene: Principios básicos del mantenimiento industrial; Fundamentos del funcionamiento de las máquinas eléctricas rotativas; Instrumentación y técnicas de medida; Diagnóstico de máquinas eléctricas rotativas mediante el análisis espectral de vibraciones; Diagnóstico de máquinas eléctricas rotativas mediante el análisis espectral de corrientes; Ensayos para el mantenimiento del sistema aislante de máquinas eléctricas rotativas; Nuevos Métodos para el diagnóstico de fallos en motores de induscción en funcionamiento.
Conference Paper
Different techniques have been developed for the early detection of rotor asymmetries in squirrel cage asynchronous motors over the last two decades. Although their reliability and the amount of information they provide about the a machine's state is indubitable, they still have a serious limitation: failure detection when the motor is driving a machine capable of producing oscillations in the load torque. In this case, the motor phase currents are modulated by torque oscillations and the information they contain about the integrity of bars and end rings is hidden or altered. In fact, it can be affirmed that almost all diagnosis methods based on spectral analysis of external electrical variables are affected by this phenomenon. In order to overcome this drawback, attempts have been made to find new procedures leading to new indicators to discriminate between the influence of the load and the rotor failure. Although certain complex systems applied to specific motors for on-line diagnosis have been successfully designed, as far as the authors know, no simple and reliable method exists for industrial diagnosis of rotor asymmetries in working cage motors under arbitrary load conditions. This paper describes a new diagnosis method, based on the measurement of the magnetic flux linked by one stator tooth, which allows perfect, simple discrimination between the actual presence of rotor asymmetries and the spurious effects caused by the oscillations in the load torque of the driven machine.
Conference Paper
The aim of this paper is to analyze theoretically and experimentally the stator current of a three-phase squirrel cage induction machine in order to show how it is influenced by rotor faults. The approach used for this study analyzes the modification introduced by n broken rotor bars in the rotor cage magnetomotive force (MMF) and then, estimates the resulting frequency spectrum in the stator current. This approach is validated in a 3 kW-230 V/400 V-50 Hz-2850 rpm-2 poles three-phase induction machine, showing the sensible frequency components to this fault condition.
Conference Paper
Motor current signature analysis (MCSA) is now widely used by industry to detect broken rotor bars and abnormal airgap eccentricity in a wide range of induction motors. The objective is to identify current components in the current signature pattern that are only a function of shorted turns and are not due to any other problem or mechanical drive characteristic. The results indicate that MCSA can diagnose shorted turns in mains and inverter fed induction motors.