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7th International DAAAM Baltic Conference
"INDUSTRIAL ENGINEERING
22-24 April 2010, Tallinn, Estonia
SMART DUST APPLICATIONS IN PRODUCTION ENVIRONMENT
Aruväli, T.; Serg, R; Preden, J. & Otto, T.
Abstract: Autonomous embedded
computers that form a sensor network can
be applied in various fields. In the domain
of industrial manufacturing sensor
networks can be used for detecting events
or phenomena at shopfloor, collecting and
processing data and transmitting sensed
information to either a central database or
directly to the handheld computer used by
the production manager. Smart dust can
be used at CNC machine tools to measure
vibration, noise and other essential
parameters. The proposed solution helps to
detect changes in shopfloor and predict
possible problems, thus avoiding
unplanned pauses in production.
Key words: smart dust, wireless sensor
network, manufacturing, e-diagnostics.
1. INTRODUCTION
High utilization and fault detection of
metal working machinery is an issue of
high importance in industrial applications.
Operation in an undesirable mode can
cause poor production quality, perversion
of material but also in extreme cases tool
failures and damages to the machinery.
Two of the last damages are especially
harmful for production, causing unplanned
breaks in production and delays in
fulfilling customer orders.
The process of developing metal working
machinery is ongoing. Building up more
sophisticated working processes, using
wear resistant tool materials, raising speeds
and powers permit the production of more
complicated parts and also shorten the time
of machining. The increased efficiency and
speed of production may also result in
faster changes in manufacturing equipment
state – the step from regular working
process to machinery unstable condition is
potentially also shorter. As the result
machinery in modern manufacturing
process requires effective on-line
monitoring and fault prediction.
Machinery monitoring options are rarely
mentioned in case of new machinery. In
case of modern manufacturing equipment a
monitoring system is assumed to be part of
the machinery. However in many cases the
manufacturing equipment can be destroyed
either because of wrong operating modes
or trivial part failures without any advance
indication of potential trouble from the
onboard monitoring system. The main
reason for this is the fact that a complex
monitoring system will add to the cost of
the machine which of course is a
competitive disadvantage in the low budget
metal working machinery market.
Machinery that is 30-40 years old is
typically quite massive, which assures
stable machining and suppresses
vibrations. These properties make also
these machines valuable and they are still
running at shopfloors tens of years into
service. The main disadvantage of such
machines lies in the fact that they are not
equipped with a monitoring system or the
functionality of the monitoring system is
too limited.
The abovementioned cases require
installation of modern wireless monitoring
system to maintain the advantages of the
existing machinery and ensure safe
operation on the manufacturing floor.
Installing a monitoring system based on
wireless sensor nodes is relatively cheap
and it can be fitted to both old and modern
manufacturing equipment.
Attaching embedded computers with a
wireless communication interface which
form a wireless sensor network (WSN)
onto low budget machinery for monitoring
machinery condition keeps the price of the
solution reasonable but provides extra
safety to existing process. The installation
cost of cable in industrial plant can vary
greatly based on the type of plant and
physical configurations. Studies have
shown that average cable installation cost
is between 10$ and 100$ per foot [1], but in
nuclear plant even 2000$ per foot.
Research in the field of wireless sensor
networks (also called smart dust) was
started as a research project in 1997 by
University of California computer science
professor Kris Pister. A smart dust mote is
a tiny computer equipped with a processor,
some memory, a wireless communication
interface, an autonomous power supply and
a set of sensors appropriate for the task at
hand. The motes can communicate with
each other and activate themselves only if
it is required by the application to prolong
battery life. At the time (this is true also
currently to a certain extent) smart dust
was very advanced compared to existing
solutions as it potentially enabled to build
networked intelligence into everything
from walls to laptop computers. In last
decade many researches have been made to
transform the dream into reality. Examples
can be brought from machinery monitoring
research community where the technology
has been applied in condition monitoring in
end-milling [2] and in drilling machine [3].
In condition monitoring applications a
parameter that reflects the state (condition)
of the machinery is monitored. Before a
condition monitoring application can be
deployed, models are developed that reflect
the correlation between the state of the
machine and the monitored parameter.
From the value of the parameter the state
of the machine is then estimated at
runtime, enabling the detection of failures
and critical modes of operation. Condition
monitoring is one of the major components
of predictive maintenance. The use of
conditional monitoring allows maintenance
to be scheduled, or other actions to be
taken to avoid the consequences of failure,
before the failure occurs. Nevertheless, a
deviation from a reference value (e.g.
temperature or vibration behaviour) must
occur to identify impeding damages.
Predictive maintenance does not predict
failure; it only helps predicting the time of
failure. The failure has already commenced
and sensor system can only measure the
deterioration of the condition. Performing
repair or maintenance operations in a
predictive manner is typically much more
cost effective than allowing the machinery
to fail.
The aim of the paper is to present concept
of measuring and identifying operation
modes of machinery for detecting
unwanted status and preventing tool
braking.
Prototype measuring devices were
designed and assembled and test
measurements conducted in controlled
conditions. Measured parameters were
acceleration for vibration detection and
acoustic signals. Experiments were
conducted in turning lathe.
2. ACCELERATION
MEASUREMENTS
2.1 Measurement method
Vibration of the unit was measured with
solid-state MEMS (Micro Electro
Mechanical System) accelerometer
LIS3LV02DQ. This device is capable of
measuring acceleration in 3 directions in
the range of +-2g at 12 bit resolution.
Gravity of Earth is included in
measurement results. The sensor type was
selected as it has a suitable measurement
range and accuracy, small footprint
(7x7x2mm), internal digital conversion
unit with noise suppression, suitable
electrical interface and is readily available
in prototyping form. Same sensor can be
used in final and optimized WSN scenario
as it has suitable electrical interface (SPI)
and very low power requirements
(0.8mA@3.3V). The sensor was interfaced
to a computer during the experiments via
the low-voltage SPI bus. An additional data
acquisition / interface board was installed
between the sensor and the main data
acquisition computer as the computer was
not equipped with the SPI interface. The
data acquisition board was a WSN node
prototype based on Atmel AVR XMEGA
microcontroller. As the data acquisition
board is essentially a full fledged WSN
node it is also capable of reading sensor
data, buffering it and later forwarding it to
computer in serial (RS232) format.
Considering the constraints of the interface
board memory, processing power and
serial communication acquisition speed
640 samples/s and 30 s measuring period
was chosen. The resulting data sets consist
of 19200 samples for each axis.
In the final and optimized WSN scenario
serial (RS232) data link will be replaced
with a wireless communication module that
is already present on prototype board.
Depending on analysis results and
firmware it is possible to transmit live
measurement information continuously or
just the identified state of the machinery
being monitored.
2.2 Measurement process
All measures were made in CNC turning
lathe 16A20F3RM132. The acceleration
sensor was bolted to CNC turning lathe
carriage and 5 sets of data acquisition
experiments were conducted. Ideal
positioning of the sensor would show
offset of the result that is caused by gravity
in one direction only. Current results show
offsets in other 2 axes also.
Test 1 and 2 were made just at empty
spindle at speed 2400 min-1. Test 3 was
made at spindle speed 600 min-1, feed rate
0.3 mm/s with real turning. Test 4 was
made at spindle speed 2400 min-1, feed rate
0.3 mm/s with real turning; this test also
includes an event of failure. Test 5 was
made at spindle speed 600 min-1, feed rate
0.3 mm/s with real turning; this test also
includes an event of failure. The result of
failures in tests 4 and 5 was tool breakage.
Tests parameters are shown in table 1.
Table 1. Acceleration tests parameters
test
no
spindle
speed
(rev/min)
feed
(mm/s)
turning
failure
1
2400
0
2
2400
0
3
600
0.3
x
4
2400
0.3
x
x
5
600
0.3
x
x
2.3 Analysis of the results
Results were analyzed in time domain.
Mean values of the acceleration series
show that offset doesn't drift and this
means that sensor was fixed reliably during
whole measuring process.
Standard ranges of the acquired data series
that are presented in table 2 shows extreme
values at test 4, but also high value in test
5. Both of these tests include tool breakage.
Other tests seem to be quite similar.
Distinction between different modes of the
turning lathe can be observed better in
graphical representation of the acceleration
values presented in figures 1-5
corresponding to test 1-5. Every figure
contains measurements of acceleration in 3
directions presented in same scale.
Table 2. Range values in every axes
test no
x axis
y axis
z axis
1
116
160
88
2
119
156
94
3
125
161
89
4
185
234
385
5
133
200
94
Tests 1 and 2 that were conducted with
exactly the same turning parameters show
that their value difference is negligible
(max 7% in Z axis). It shows that test
results are repeatable and test values are
reliable.
Fig. 1. Acceleration test 1
Fig. 2. Acceleration test 2
Fig. 3. Acceleration test 3
Comparison of tests 3 and 5 illustrates the
difference between normal operation and
failure during operation. Tests 3 and 5
were made in same operational parameters;
the only difference was the failure of the
tool. Y axis value was 24% higher in fault
situation than in normal operation mode.
This distinction allows fault identification.
Comparison of tests 4 and 5 illustrates
rapidly growing vibration in breaking
situation in higher spindle speeds. With
higher spindle speeds the failure pattern is
more distinct.
Fig. 4. Acceleration test 4
Fig. 5. Acceleration test 5
2.4 Conclusion of the measurement
result analysis
It is possible to identify different modes of
operation by measuring acceleration in
turning lathe carriage. The identification
task is simpler at higher spindle speeds as
the pattern is more distinct in that case.
3. ACOUSTIC MEASUREMENTS
3.1 Measurement method and
description
Acoustic signal of the unit was measured
with SM58 microphone and the analogue
signal was converted to digital using
Roland Edirol UA-25EX audio signal
processor. The digitized signal was
recorded in a PC. All measures were made
in CNC turning lathe 16A20F3RM132.
The microphone was positioned near the
cutting place. The acoustic signal was
sampled at a sampling rate of 22050 Hz
and recorded to a wav file. Data was
sampled during a turning work cycle
(starting up engines, turning, turning fault
and turning off engine).
3.2 Measurement results
Operation mode classification was made by
applying spectral analysis to the sampled
signal. Fourier transforms were performed
on sections of recorded samples acquired
during different modes of operation and the
resulting frequency spectrums were
compared with each other.
Figure 6 represents the spectrums of
signals acquired in different modes of
operation. In mode 1 feed engine works
only, in mode 2 spindle engine is turned
on, in mode 3 the lathe is normal
operational mode and in mode 4 a fault
occurs.
01000 2000 3000 4000 5000 6000 7000 8000
0
0.02
0.04
0.06 Reziim 1
01000 2000 3000 4000 5000 6000 7000 8000
0
0.02
0.04
0.06 Reziim 2
01000 2000 3000 4000 5000 6000 7000 8000
0
0.02
0.04
0.06
0.08 Treimine
01000 2000 3000 4000 5000 6000 7000 8000
0
0.05
0.1
0.15
0.2 Treimise viga
Fig. 6. Regimes 1-4 in turning
The spectrums of signals acquired in
modes 2 and 3 are similar and
distinguishing them from each other is
difficult. For that reason the spectrum for
mode 3 is discarded and only the spectrums
of signals acquired in modes 1, 2 and 4 are
analysed. In figure 7 acoustic signals are
measured with 0.2 s interval. The whole
length of the test was 40 s.
Figure 7 shows different pattern of the
signal in feed engine working mode,
turning mode and in the occurrence of a
fault.
0510 15 20 25 30 35 40
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
helisignaal reziim 1 reziim 2 t reimise viga
Fig. 7. Acoustic signals in different modes
The location and number of acoustic
sensor(s) (microphone) plays an essential
role in the signal evaluation. It can be
predicted that increasing the number of
sensors will yield better results.
4. MONITORING WITH SMART
DUST
The tests described in the paper were
performed using wired sensors. For real
applications in manufacturing floor it is
essential to employ wireless sensors that
are integrated to an e-manufacturing
system [4]. As suggested in the introduction
wireless sensors or smart dust motes can be
used in such monitoring applications in
addition to the wide range of other smart
dust potential applications [5]. Smart dust
motes can be equipped with a wide range
of sensors, so depending on the application
the properties of a smart dust mote can
vary substantially as the processing unit of
the mote may be also different to be able to
process the data collected by the sensors.
For monitoring various types of machinery
(and different properties of specific
manufacturing equipment), different
sensors must be used and the motes must
be assembled correspondingly from
modules [6].
5. FURTHER RESEARCH
The test results presented in the paper were
just a little touch of machinery monitoring.
Further research is required to develop and
implement practical solutions.
1. The optimal sensor placement must be
determined for every type of machine in
order to acquire the parameters of interest
2. Manufacturing equipment must be
categorized from the monitoring
perspective to develop and employ fixed
configurations of monitoring equipment on
different machines.
3. In order to determine tool wearing
pattern experiments must be conducted
also with different tool wear levels.
6. CONCLUSION
Experiments showed that different
operational modes of manufacturing
equipment can be determined using basic
sensors and signal processing methods.
Measurements made with accelerometer
show vibration range that allows to
recognize fault situation from normal
operation. Acoustic measurements allow to
distinguish idle operation, normal
operation and fault situation.
In order to implement an automated
monitoring system for manufacturing
equipment the patterns for different modes
of operation must be determined initially,
after which a WSN can be used to detect
the modes of interest.
7. ACKNOWLEDGEMENT
This research was supported by Estonian
Ministry of Education and Research
Project SF0140113Bs08, ESF Grant
F7852, Innovative Manufacturing
Engineering Systems Competence Centre
IMECC co-financed by EAS and European
Union Regional Development Fund
(project EU30006), and Doctoral school of
energy and geotechnology II.
8. REFERENCES
1. Tiwaria, A. and Lewis, F. L. Wireless
sensor network for machine condition
based maintenance. Proc. 8th Int. Conf.
Control, Automation, Robotics and Vision,
2004.
2. Wright, P., Dornfeld, D. and Ota, N.
Condition monitoring in end-milling using
wireless sensor networks (WSNs). Trans.
NAMRI/SME, 2008, 36.
3. Shin, B.; Kim, G.; Choi, J.; Jeon, B.;
Lee, H.; Cho M.; Han, J. and Park, D. A
Web-based machining process monitoring
system for E-manufacturing
implementation. J Zhejiang Univ.
SCIENCE A, 2006, 7, 1467-1473.
4. Koç, M.; Ni., J; Lee, J. and
Bandyopadhyay, P. Introduction to e-
manufacturing. Int. J. Agile
Manufacturing, 2003, 6, 97-1 – 97-9.
5. Haenggi, M. Opportunities and
challenges in wireless sensor networks. In
Smart dust: sensor network applications,
architecture and design (Mahgoub, I. and
Ilyas, M.), Taylor & Francis Group, Boca
Raton, 2006, 1-1 - 1-14.
6. Sarkans, M.; Preden J.; Otto, T. and
Reinson, T. Smart dust based modular
laboratory kit for monitoring workshop
machinery. In 8th International Workshop
on Research and Education in
Mechatronics 2007 (Tamre, M.), Tallinn
University of Technology Press, Tallinn,
2007, 299 - 308.
8. ADDITIONAL DATA ABOUT
AUTHORS
Tanel Aruväli, Tallinn University of
Technology, Dep. of Machinery, Ehitajate
tee 5, 19086 Tallinn, Estonia,
tanelaruvali@hot.ee
Risto Serg, Tallinn University of
Technology, Research Laboratory for
Proactive Technologies, Dep of Computer
Control, Ehitajate tee 5, 19086 Tallinn,
Estonia, risto.serg@dcc.ttu.ee
Jürgo Preden, Tallinn University of
Technology, Research Laboratory for
Proactive Technologies, Dep of Computer
Control, Ehitajate tee 5, 19086 Tallinn,
Estonia, jurgo.preden@ttu.ee
