Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications

564 Pages·2019·17.52 MB·English

by  Edwin Lughofer

Edwin Lughofer 
Moamar Sayed-Mouchaweh Editors
Predictive
Maintenance
in Dynamic
Systems
Advanced Methods, Decision Support
Tools and Real-World Applications
Predictive Maintenance in Dynamic Systems
Edwin Lughofer • Moamar Sayed-Mouchaweh
Editors
Predictive Maintenance
in Dynamic Systems
Advanced Methods, Decision Support Tools
and Real-World Applications
123
Editors
EdwinLughofer MoamarSayed-Mouchaweh
FuzzyLogicLaboratoriumLinz-Hagenberg InstituteMines-TelecomLilleDouai
DepartmentofKnowledge-Based Douai,France
MathematicalSystems
JohannesKeplerUniversityLinz
Linz,Austria
ISBN978-3-030-05644-5 ISBN978-3-030-05645-2 (eBook)
https://doi.org/10.1007/978-3-030-05645-2
LibraryofCongressControlNumber:2019931901
©SpringerNatureSwitzerlandAG2019
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof
thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,
broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation
storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology
nowknownorhereafterdeveloped.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbook
arebelievedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsor
theeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinorforany
errorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictional
claimsinpublishedmapsandinstitutionalaffiliations.
ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG.
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
Toourfamiliesandfriends
Preface
Duringrecentyears,rapidtechnologicaldevelopmentsandbreakthroughsinvarious
industrialautomatizationprocesseswiththesupportofmodernmachines,bigdata
storages,andcloudsofcomputersoperatinginparallelledtoasignificantincrease
in system complexity and dynamic processes. This makes a manual supervision
andmaintenanceofmachines,systemcomponents,andproductionchainsmoreand
more unaffordable and thus unrealistic to be conducted in a reasonable amount of
timewithreasonableeffortsandcostsforcompanies.
Therefore, automated predictive maintenance (APdM) has more and more
becomeacentralcornerstoneintoday’sindustrialapplicationsandsystemsranging
from online manufacturing rails and production lines through (cyber) security
problems and infrastructure management to energy fabrication, maritime systems,
and exploitation facilities. This is because APdM addresses not only strategies
for the early detection and prediction of significant machine wearing towards
componentfailures,degradedperformanceofthesystem,undesiredsituationsand
occurrences, or downtrends in product quality but also for taking appropriate
actions upon the recognition and prediction of such occasions. Such actions are
indispensable for reducing waste, repair, and production costs, or even customer
complaints,andthus,inthelongrun,alsoforincreasingtheincomeofcompanies;
forguaranteeinghigherqualityofproductionitems,networkfunctionality,software,
and user front ends; and finally for reducing the pollution of the environment.
In the extreme case of catastrophic system failures, any severe damage to the
infrastructure, machine, or system and severe risks for operators working with the
systemcanbeavoidedbypredictivemaintenance.
The necessity of APdM in theory and practice is reflected in several method-
ologicalandapplication-orienteddevelopmentsduringthelast15–20years,where,
according to the ISI Web of Knowledge/Science database (Thomson Reuters)—
www.webofknowledge.com—the number of publications permanently grew from
around100peryeararoundthebeginningofthe2000suptomorethan500peryear
around2017,andthenumberofcitationsgrewevenmoreintensively.Furthermore,
APdM became an essential component in today’s Industry 4.0 environments and
applications, and several objectives with associated calls have been established
vii
viii Preface
during the recent years under the umbrella of the Horizon 2020 Framework
Programme“Nanotechnologies,AdvancedMaterials,AdvancedManufacturingand
Processing,andBiotechnology.”
In a typical predictive maintenance framework, embedded system models play
a key role for producing (quality) forecasts, for indicating arising problems and
faults at an early stage, or for conducting any deeper diagnosis about upcoming
expected (as predicted) anomalous process behaviors in various forms. The high
dynamicsintoday’sprocessesorpartsofprocessesoftenhastheeffectthatalready
modeled/learneddependenciesbecomeoutdated,whichrequiressystemmodelsto
self-adaptovertimeinordertomaintaintheirpredictiveperformanceandtoexpand
their “knowledge” and “validation range.” This is hardly considered in the current
state of the art of predictive maintenance; therefore, it is a central aspect in this
booktoshownewtrendsinthisdirection—infact,mostofthechaptersaredealing
with (data-driven) modeling, optimization, and control (MOC) strategies, which
possesstheabilitytobetrainableandadaptableontheflybasedonchangingsystem
behaviorandnonstationaryenvironmentalinfluences.
Apartfromthis,severalnewapplicationsinthecontextofpredictivemaintenance
aswellascombinationsofMOCmethodologiestosuccessfullyestablishpredictive
maintenance are demonstrated in this book. According to the essential steps in
predictivemaintenancesystemsfromearlyanomalyandfaultdetectionduringthe
processthroughtheprognosticsofeventuallyarisingproblemsinthe(near)future
to their diagnosis and proper reactions on these (through optimization, control for
repair,andself-healing),thebookisstructuredintothreemainparts,whereineach
ofthem,importantreal-worldsystemsandapplicationscenariosarediscussed:
(cid:129) Anomalydetectionandlocalization
(cid:129) Prognosticsandforecasting
(cid:129) Diagnosis,optimization,andcontrol
Furthermore, the first three chapters round off the whole book by discussing
important aspects, principal concepts, and requirements and which are of general
relevance in predictive maintenance systems and thus can be of significant impor-
tanceinanyofthethreemethodologicalsteps(bookparts).
Finally,theeditorsareverygratefultoallauthorsandreviewersforcontributing
withsubstantialandveryvaluablematerialtomakethisvolumebecomealiveandto
setanothercornerstoneintheresearchandpublicationhistoryofpredictivemainte-
nancemethodologiesandapplications.Thefirsteditoracknowledgesthesupportby
the “LCM—K2 Center for Symbiotic Mechatronics” within the framework of the
Austrian COMET-K2 program. We also acknowledge Mary E. James and Menas
Donald Kiran for establishing the contract with Springer and supporting us in any
organizational aspects. We hope that the volume will be a useful basis for further
fruitful investigations and fresh ideas as well as a motivation and inspiration for
newcomerstojointhisimportantandstillemergingfieldofresearch.
Linz,Austria EdwinLughofer
Douai,France MoamarSayed-Mouchaweh
October2018
Contents
Prologue:PredictiveMaintenanceinDynamicSystems..................... 1
EdwinLughoferandMoamarSayed-Mouchaweh
SmartDevicesinProductionSystemMaintenance .......................... 25
Eike Permin, Florian Lindner, Kevin Kostyszyn, Dennis Grunert,
KarlLossie,RobertSchmitt,andMartinPlutz
On the Relevance of Preprocessing in Predictive Maintenance
forDynamicSystems ............................................................ 53
CarlosCernuda
PartI AnomalyDetectionandLocalization
AContext-SensitiveFrameworkforMiningConceptDriftingData
Streams............................................................................ 97
ChamariI.KithulgodaandRusselPears
Online Time Series Changes Detection Based on Neuro-Fuzzy
Approach.......................................................................... 131
Yevgeniy Bodyanskiy, Artem Dolotov, Dmytro Peleshko,
YuriyRashkevych,andOlenaVynokurova
Early Fault Detection in Reciprocating Compressor Valves
byMeansofVibrationandpVDiagramAnalysis............................ 167
KurtPichler
ANewHilbert-HuangTransformTechniqueforFaultDetectionin
RollingElementBearings ....................................................... 207
ShazaliOsmanandWilsonWang
Comparison of Genetic and Incremental Learning Methods
forNeuralNetwork-BasedElectricalMachineFaultDetection ............ 231
DanielLeite
ix
x Contents
EvolvingFuzzyModelforFaultDetectionandFaultIdentification
ofDynamicProcesses............................................................ 269
GoranAndonovski,SašoBlažicˇ,andIgorŠkrjanc
AnOnlineRFIDLocalizationintheManufacturingShopfloor ............ 287
Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Qing Cai,
andHuangSheng
PartII PrognosticsandForecasting
Physical Model-Based Prognostics and Health Monitoring
toEnablePredictiveMaintenance.............................................. 313
TiedoTingaandRichardLoendersloot
On Prognostic Algorithm Design and Fundamental Precision
LimitsinLong-TermPrediction................................................ 355
MarcosE.OrchardandDavidE.Acuña
Performance Degradation Monitoring and Quantification:
AWastewaterTreatmentPlantCaseStudy................................... 381
Iñigo Lecuona, Rosa Basagoiti, Gorka Urchegui, Luka Eciolaza,
UrkoZurutuza,andPeterCraamer
Fuzzy Rule-Based Modeling for Interval-Valued Data:
AnApplicationtoHighandLowStockPricesForecasting ................. 403
LeandroMacielandRosangelaBallini
PartIII Diagnosis,OptimizationandControl
Reasoning from First Principles for Self-adaptive
andAutonomousSystems....................................................... 427
FranzWotawa
DecentralizedModularApproachforFaultDiagnosisofaClass
ofHybridDynamicSystems:ApplicationtoaMulticellularConverter... 461
MoamarSayed-Mouchaweh
AutomatedProcessOptimizationinManufacturingSystemsBased
onStaticandDynamicPredictionModels .................................... 485
EdwinLughofer,Alexandru-CiprianZavoianu,MahardhikaPratama,
andThomasRadauer
Distributed Chance-Constrained Model Predictive Control for
Condition-BasedMaintenancePlanningforRailwayInfrastructures..... 533
Zhou Su, Ali Jamshidi, Alfredo Núñez, Simone Baldi,
andBartDeSchutter
Index............................................................................... 555
Contributors
David E. Acuña Department of Electrical Engineering, Faculty of Mathematical
andPhysicalSciences,UniversityofChile,Santiago,Chile
Goran Andonovski Faculty of Electrical Engineering, University of Ljubljana,
Ljubljana,Slovenia
AndriAshfahani NanyangTechnologicalUniversity,Singapore,Singapore
SimoneBaldi DelftCenterforSystemsandControl,Delft,TheNetherlands
RosangelaBallini InstituteofEconomics,UniversityofCampinas,Campinas,SP,
Brazil
Rosa Basagoiti Faculty of Engineering, Mondragon Unibertsitatea, Arrasate -
Mondragon,Spain
SašoBlažicˇ FacultyofElectricalEngineering,UniversityofLjubljana,Ljubljana,
Slovenia
YevgeniyBodyanskiy KharkivNationalUniversityofRadioElectronics,Kharkiv,
Ukraine
QingCai NanyangTechnologicalUniversity,Singapore,Singapore
CarlosCernuda BCAM-BasqueCenterforAppliedMathematics,Bilbao,Spain
FacultyofEngineering(MU-ENG),MondragonUnibertsitatea,Arrasate,Spain
PeterCraamer MSIGrupo,Andoain,Spain
BartDeSchutter DelftCenterforSystemsandControl,Delft,TheNetherlands
Artem Dolotov Kharkiv National University of Radio Electronics, Kharkiv,
Ukraine
Luka Eciolaza Faculty of Engineering, Mondragon Unibertsitatea, Arrasate -
Mondragon,Spain
xi