Rotating Machinery and Signal Processing

142 Pages·2019·15.63 MB·English

by  Ahmed Felkaoui

Applied Condition Monitoring
Ahmed Felkaoui
Fakher Chaari
Editors
Mohamed Haddar
Rotating
Machinery and
Signal Processing
Proceedings of the First Workshop
on Signal Processing Applied to
Rotating Machinery Diagnostics,
SIGPROMD’2017, April 09–11, 2017,
Setif, Algeria
Applied Condition Monitoring
Volume 12
Series editors
Mohamed Haddar, National School of Engineers of Sfax, Tunisia
Walter Bartelmus, Wrocław University of Technology, Poland
Fakher Chaari, National School of Engineers of Sfax, Tunisia
e-mail: [email protected]
Radoslaw Zimroz, Wrocław University of Technology, Poland
The book series Applied Condition Monitoring publishes the latest research and
developmentsinthefieldofconditionmonitoring,withaspecialfocusonindustrial
applications. It covers both theoretical and experimental approaches, as well as a
range of monitoring conditioning techniques and new trends and challenges in the
field. Topics of interest include, but are not limited to: vibration measurement and
analysis;infrared thermography;oilanalysisandtribology;acoustic emissionsand
ultrasonics;andmotorcurrentanalysis.Bookspublishedintheseriesdealwithroot
cause analysis, failure and degradation scenarios, proactive and predictive
techniques, and many other aspects related to condition monitoring. Applications
concern different industrial sectors: automotive engineering, power engineering,
civil engineering, geoengineering, bioengineering, etc. The series publishes
monographs, edited books, and selected conference proceedings, as well as
textbooks for advanced students.
More information about this series at http://www.springer.com/series/13418
Ahmed Felkaoui Fakher Chaari
(cid:129)
Mohamed Haddar
Editors
Rotating Machinery
and Signal Processing
Proceedings of the First Workshop on Signal
Processing Applied to Rotating Machinery
’
Diagnostics, SIGPROMD 2017,
–
April 09 11, 2017, Setif, Algeria
123
Editors
Ahmed Felkaoui MohamedHaddar
Institute of Optics andPrecision Mechanics National School ofEngineers of Sfax
University Ferhat Abbas Sfax, Tunisia
Sétif, Algeria
Fakher Chaari
National School ofEngineers of Sfax
Sfax, Tunisia
ISSN 2363-698X ISSN 2363-6998 (electronic)
AppliedCondition Monitoring
ISBN978-3-319-96180-4 ISBN978-3-319-96181-1 (eBook)
https://doi.org/10.1007/978-3-319-96181-1
LibraryofCongressControlNumber:2018948634
©SpringerInternationalPublishingAG,partofSpringerNature2019
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Preface
The first workshop on Signal Processing Applied to Rotating Machinery
Diagnostics (SIGPROMD’2017) was held in Setif, Algeria, in April 2017. This
event was organized jointly by the Applied Precision Mechanics Laboratory
(LMPA) of the Institute of Precision Mechanics, University of Setif, Algeria; and
theMechanics,ModelingandManufacturingLaboratory(LA2MP)oftheNational
School of Engineers of Sfax, Tunisia.
Allthechaptersincludedinthisbookwererigorouslyreviewedbytworeferees.
Our thanks go to all reviewers of the 12 papers composing this proceeding pub-
lished under Applied Condition Monitoring book series.
It is well known that rotating machinery gives rise to vibrations and conse-
quentlynoise.Vibrationsignaturedependsonthesettingupandthehealthstatusof
each machine. A change in the vibration signature induced by a change in the
machinestateisapowerfulmeantodetectincipientdefectsbeforetheyevolveand
become critical. Vibration signals collected from machines should be processed in
ordertoextractstatefeatureswhicharecomparedtoreferencevalues.Theobjective
of the workshop was to gather researchers from both laboratories to discuss latest
advances in signal processing dedicated to rotating machinery. It was a forum to
exchangeideasanddevelopmentsinthisfield.Themaintopicsthatwerediscussed
during the workshop through the presented chapters are:
– Noise and vibration of machines
– Condition monitoring in non-stationary operations
– Vibro-acoustic diagnosis of machinery
– Signal processing
– Pattern recognition
– Monitoring and diagnostic systems
– Modeling of dynamics and faults in machinery
v
vi Preface
The editors would like to thank all participants in SIGPROMD’2017 for their
valuable contribution to this book. They hope that the readers can find what they
expectinthefieldofsignalprocessingdedicatedtomachinerydiagnostics.Finally,
manythanksgotoSpringerforofferingthisopportunitytopublishtheproceedings
of the workshop.
Setif/Sfax Ahmed Felkaoui
2018 Fakher Chaari
Mohamed Haddar
Contents
Feature Selection Scheme Based on Pareto Method for Gearbox
Fault Diagnosis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
RidhaZiani,HafidaMahgoun,SemcheddineFedala,andAhmedFelkaoui
Intelligent Gear Fault Diagnosis in Normal and Non-stationary
Conditions Based on Instantaneous Angular Speed, Differential
Evolution and Multi-class Support Vector Machine. . . . . . . . . . . . . . . . 16
Semchedine Fedala, Didier Rémond, Ahmed Felkaoui,
and Houssem Selmani
Effect of Input Data on the Neural Networks Performance Applied
in Bearing Fault Diagnosis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Hocine Fenineche, Ahmed Felkaoui, and Ali Rezig
Bearing Diagnostics Using Time-Frequency Filtering and EEMD . . . . . 44
Hafida Mahgoun and Ridha Ziani
TheTime-FrequencyFiltering(TFF)MethodUsedinEarlyDetection
of Gear Faults in Variable Load and Dimensions Defect . . . . . . . . . . . . 56
Hafida Mahgoun, Fakher Chaari, Ahmed Felkaoui, and Mohamed Haddar
Comparison Between Hidden Markov Models and Artificial Neural
Networks in the Classification of Bearing Defects . . . . . . . . . . . . . . . . . 68
Miloud Sedira, Ridha Ziani, and Ahmed Felkaoui
On-line Adaptive Scaling Parameter in Active Disturbance
Rejection Controller. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Maroua Haddar, S. Caglar Baslamisli, Fakher Chaari,
and Mohamed Haddar
Modal Analysis of the Clutch Single Spur Gear Stage System
with Eccentricity Defect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Ahmed Ghorbel, Moez Abdennadher, Lassâad Walha, Becem Zghal,
and Mohamed Haddar
vii
viii Contents
Estimation of Road Disturbance for a Non Linear Half Car Model
Using the Independent Component Analysis . . . . . . . . . . . . . . . . . . . . . 96
Dorra Ben Hassen, Mariem Miladi, Mohamed Slim Abbes,
S. Caglar Baslamisli, Fakher Chaari, and Mohamed Haddar
Transfer Path Analysis of Planetary Gear with Mechanical
Power Recirculation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
Ahmed Hammami, Alfonso Fernandez del Rincon, Fakher Chaari,
Fernando Viadero Rueda, and Mohamed Haddar
Modeling the Transmission Path Effect in a Planetary Gearbox . . . . . . 116
Oussama Graja, Bacem Zghal, Kajetan Dziedziech, Fakher Chaari,
Adam Jablonski, Tomasz Barszcz, and Mohamed Haddar
Dynamic Behavior of Spur Gearbox with Elastic Coupling
in the Presence of Eccentricity Defect Under Acyclism Regime . . . . . . . 123
Atef Hmida, Ahmed Hammami, Fakher Chaari,
Mohamed Taoufik Khabou, and Mohamed Haddar
Author Index.. .... .... .... ..... .... .... .... .... .... ..... .... 133
Feature Selection Scheme Based on Pareto
Method for Gearbox Fault Diagnosis
Ridha Ziani(&), Hafida Mahgoun,
Semcheddine Fedala, and Ahmed Felkaoui
Laboratory of AppliedPrecision Mechanics,
Institute of Optics andPrecision Mechanics, Ferhat AbbesUniversity,
Setif 1,19000Setif,Algeria
[email protected]
Abstract. Faultdiagnosisbasedonpatternrecognitionapproachhasthreemain
stepsviz.featureextraction,sensitivefeaturesselection,andclassification.The
vibrationsignalsacquiredfromthesystemunderstudyareprocessedforfeature
extraction using different signal processing methods. Followed by feature
selection process, classification is performed. The challenge is to find good
features that discriminate the different fault conditions of the system, and
increase the classification accuracy. This paper proposes the use of Pareto
method for optimal feature subset selection from the pool of features. To
demonstrate the efficiency and effectiveness of the proposed fault diagnosis
scheme, numerical analyses have been performed using the Westland data set.
TheWestlanddatasetconsistsofvibrationdatacollectedfromaUSNavyCH-
46E helicopter gearbox in healthy and faulty conditions. First, features are
extracted from vibration signals in time, spectral, and time-scale domain, then
ranked accordingto three different criterions namely: Fisher score, correlation,
andSignaltoNoiseRatio(SNR).Afterword,dataformedbyonlytheselected
featuresisusedasinputfortheclassificationproblem.Theclassificationtaskis
achieved using Support Vector Machines (SVM) method. The proposed fault
diagnosis scheme has shown promising results. Using only the feature subset
selected byParetomethodwith Fisher criterion, SVMsachieved100%correct
classification.
(cid:1) (cid:1)
Keywords: Signal processing SupportVector Machine Featureselection
(cid:1)
Vibration Fault diagnosis
1 Introduction
The gears are one of the major components of rotating machines, and proper mainte-
nance of gear system is very essential to ensure reliability, safety, and performance of
machines.Themost ofthedevelopedmethodsforfaultdiagnosisofthesesystemsare
based on pattern recognition approach (Rafiee et al. 2007, 2010; Gryllias and
Antoniadis2012;Zhangetal.2013;Zianietal.2017).Theadvantageofthisapproach
isthatitdoesn’trequirelargeprioriknowledgeoftheprocessunderstudy.Inthiscase,
the diagnosis is assimilated to a classification problem (healthy or faulty condition).
©SpringerInternationalPublishingAG,partofSpringerNature2019
A.Felkaouietal.(Eds.):SIGPROMD’2017,ACM12,pp.1–15,2019.
https://doi.org/10.1007/978-3-319-96181-1_1