Table Of ContentSpringer Theses
Recognizing Outstanding Ph.D. Research
Alaa Abdulhady Jaber
Design of an
Intelligent
Embedded System
for Condition
Monitoring of an
Industrial Robot
Springer Theses
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Alaa Abdulhady Jaber
Design of an Intelligent
Embedded System
for Condition Monitoring
of an Industrial Robot
Doctoral Thesis accepted by
Newcastle University, UK
123
Author Supervisor
Dr. AlaaAbdulhady Jaber Prof. RobertBicker
Schoolof MechanicalandSystems Schoolof MechanicalandSystems
Engineering Engineering
Newcastle University Newcastle University
Newcastle uponTyne Newcastle uponTyne
UK UK
and
MechanicalEngineering Department
University of Technology
Baghdad
Iraq
ISSN 2190-5053 ISSN 2190-5061 (electronic)
SpringerTheses
ISBN978-3-319-44931-9 ISBN978-3-319-44932-6 (eBook)
DOI 10.1007/978-3-319-44932-6
LibraryofCongressControlNumber:2016949598
©SpringerInternationalPublishingSwitzerland2017
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This thesis is dedicated to my beloved father,
the martyr Abdulhady Jaber, who was given
the honour of martyrdom on 29 December
1986, and also to all the martyrs of Iraq, who
sacrificed themselves in order to keep Iraq
and its people secure and safe. All the love,
appreciation and respect are due to them.
’
Supervisor s Foreword
The University of Newcastle upon Tyne has enthusiastically nominated this Ph.D.
thesisfora2016SpringerThesesseriesbecauseitwasconsideredtobeanexcellent
example of independent and well-conducted research. Dr. Alaa Abdulhady Jaber
joinedNewcastleUniversityasapostgraduatestudentinJanuary2012tostudyfor
Ph.D. and quickly defined the area of his research topic in the initial months, and
havingundertakenacriticalreviewoftheresearchliteratureintheareaofcondition
monitoring, he established that very little published research was available on its
applicationtoindustrialrobots.Havingrevealedasignificantresearchopportunity,
Dr. Jaber then committed himself wholeheartedly to his studies, tackling the
challengingobjectivesinaveryprofessionalandsystematicway,ensuringthework
was accomplished in a timely manner.
Dr. Jaber has successfully designed and commissioned an intelligent health
monitoring system, specifically for use on an industrial robot, which is able to
predict the onset of mechanical wear and failure in the joints of the geared trans-
missions. The design of the two-level embedded wireless condition monitoring
system utilizes a 3-axis accelerometer to capture the operational arm vibration in
real time which it then analyses against healthy signature data to establish the
emergence of a potential fault. The developed system is capable of monitoring a
number of robots simultaneously and also lends itself very well for application on
anypowertransmissionequipment inwhichtheloads andspeeds arenotconstant,
particularly where access is restricted. As such, this provides significant scope for
future research.
Theworkpresentedinthisthesisrepresentsthreesignificantachievements.First
isthedevelopmentofaconditionmonitoringalgorithmbasedonvibrationanalysis
of an industrial robot for fault detection and diagnosis. The combined use of a
statisticalcontrolchartwithtime-domainsignalanalysisfordetectingamechanical
faultviaanarm-mountedwirelessprocessorsystemrepresentsthefirststageoffault
detection. Second, the design and development of a sophisticated embedded
microprocessor base station (the second stage) for the online processing of the
intelligent condition monitoring algorithm, and third, the implementation of a
vii
viii Supervisor’sForeword
discretewavelettransform,usinganartificialneuralnetwork,withstatisticalfeature
extraction for the robot fault diagnosis in which the vibration signals are decom-
posed into eight levels of wavelet coefficients. The strategy for detecting and
analysing potential faults in the robot used in the case study was considered both
practical and inexpensive and as such provides a significant contribution towards
solving complex mechanical faults in a wide range of power transmission systems
andopensuptheopportunityfortakingthisresearchforwardtoaddressreal-world
applications.
At this time, Dr. Jaber has published the results of his work at six International
Conferences and already has six Journal publications to his name, with a further
four submitted and under review, and three more in preparation. He has also
received a number of awards and certificates of merit from the Iraqi Ministry of
Higher Education for his achievements, in testimony to his efforts to successfully
promote his work.
Isupervisedover25Ph.D.studentsovertheyearsatNewcastleandamproudto
say that I consider Dr. Jaber as being one of the most dedicated and committed
studentswhichitwasmypleasuretosupervise.Hiscontinuedenthusiasmandhard
work enabled him to overcome the many technical challenges posed, and in
summary,Ifeelthisisasufficientproofthatthisworkisworthyofanominationfor
the Springer Theses and take this opportunity to wish him every success for the
future.
Newcastle upon Tyne, UK Prof. Robert Bicker
May 2016
Abstract
Industrial robots have long been used in production systems in order to improve
productivity, quality and safety in automated manufacturing processes. There are
significant implications for operator safety in the event of a robot malfunction or
failure,andanunforeseenrobotstoppage,duetodifferentreasons,hasthepotential
to cause an interruption in the entire production line, resulting in economic and
production losses. Condition monitoring (CM) is a type of maintenance inspection
technique by which an operational asset is monitored and the data obtained are
analysed to detect signs of degradation, diagnose the causes of faults and thus
reduce maintenance costs. So, the main focus of this research is to design and
develop an online, intelligent CM system based on wireless embedded technology
to detect and diagnose the most common faults in the transmission systems (gears
and bearings) of the industrial robot joints using vibration signal analysis.
Tothisendanold,butoperational,PUMA560robotwasutilizedtosynthesizea
number of different transmission faults in one of the joints (3—elbow), such as
backlashbetweenthegearpair,geartoothandbearingfaults.Atwo-stagecondition
monitoring algorithm is proposed for robot health assessment, incorporating fault
detectionandfaultdiagnosis.Signalprocessingtechniquesplayasignificantrolein
building any condition monitoring system, in order to determine fault–symptom
relationships,anddetectabnormalitiesinrobothealth.Faultdetectionstageisbased
ontime-domainsignalanalysisandastatisticalcontrolchart(SCC)technique.For
accurate fault diagnosis in the second stage, a novel implementation of a
time-frequency signal analysis technique based on the discrete wavelet transform
(DWT) is adopted. In this technique, vibration signals are decomposed into eight
levels of wavelet coefficients and statistical features, such as standard deviation,
kurtosis and skewness, are obtained at each level and analysed to extract the most
salientfeaturerelatedtofaults;theartificialneuralnetwork(ANN)isthenusedfor
fault classification. A data acquisition system based on National Instruments
(NI) software andhardwarewasinitiallydevelopedfor preliminary robotvibration
analysis and feature extraction. The transmission faults induced in the robot can
change the captured vibration spectra, and the robot’s natural frequencies were
established using experimental modal analysis, and also the fundamental fault
ix
x Abstract
frequencies for the gear transmission and bearings were obtained and utilized for
preliminary robot condition monitoring.
In addition to simulation of different levels of backlash fault, gear tooth and
bearingfaultswhichhavenotbeenpreviouslyinvestigatedinindustrialrobots,with
several levels of severity, were successfully simulated and detected in the robot’s
joint transmission. The vibration features extracted, which are related to the robot
healthy state and different fault types, using the data acquisition system were
subsequently used in building the SCC and ANN, which were trained using part
of the measured data set that represents the robot's operating range. Another set of
data,notusedwithinthetrainingstage,wasthenutilizedforvalidation.Theresults
indicate the successful detection and diagnosis of faults using the key extracted
parameters. A wireless embedded system based on the ZigBee communication
protocol was designed for the application of the proposed CM algorithm in real
time,usinganArduinoDUEasthecoreofthewirelesssensorunitattachedonthe
robot arm. A Texas Instruments digital signal processor (TMS320C6713 DSK
board)wasusedasthebasestationofthewirelesssystemonwhichtherobot’sfault
diagnosis algorithm is run. To implement the two stages of the proposed CM
algorithmonthedesignedembeddedsystem,softwarebasedontheCprogramming
language has been developed. To demonstrate the reliability of the designed
wirelessCMsystem, experimentalvalidationswereperformed,andhighreliability
was shown in the detection and diagnosis of several seeded faults in the robot.
Optimistically,theestablishedwirelessembeddedsystemcouldbeenvisagedfor
faultdetectionanddiagnosticsonanytypeofrotatingmachine,withthemonitoring
system realized using vibration signal analysis. Furthermore, with some modifica-
tions to the system’s hardware and software, different CM techniques, such as
acoustic emission (AE) analysis or motor current signature analysis (MCSA), can
be applied.
(cid:1) (cid:1)
Keywords Conditionmonitoring Industrialrobotfaultdetectionanddiagnosis
(cid:1) (cid:1) (cid:1)
Experimental modal analysis Embedded system Wireless Vibration signal
(cid:1) (cid:1) (cid:1)
analysis Wavelet transform Statistical control chart Artificial neural network
