Monitoring Multimode Continuous Processes: A Data-Driven Approach PDF
Preview Monitoring Multimode Continuous Processes: A Data-Driven Approach
Studies in Systems, Decision and Control 309 Marcos Quiñones-Grueiro Orestes Llanes-Santiago Antônio José Silva Neto Monitoring Multimode Continuous Processes A Data-Driven Approach Studies in Systems, Decision and Control Volume 309 Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland The series “Studies in Systems, Decision and Control” (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control–quickly, up to date and withahighquality.Theintentistocoverthetheory,applications,andperspectives on the state of the art and future developments relevant to systems, decision making,control,complexprocessesandrelatedareas, asembeddedinthefieldsof engineering,computerscience,physics,economics,socialandlifesciences,aswell astheparadigmsandmethodologiesbehindthem.Theseriescontainsmonographs, textbooks, lecture notes and edited volumes in systems, decision making and control spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular valuetoboththecontributorsandthereadershiparetheshortpublicationtimeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. ** Indexing: The books of this series are submitted to ISI, SCOPUS, DBLP, Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and Springerlink. More information about this series at http://www.springer.com/series/13304 ñ Marcos Qui ones-Grueiro (cid:129) Orestes Llanes-Santiago (cid:129) ô é Ant nio Jos Silva Neto Monitoring Multimode Continuous Processes A Data-Driven Approach 123 Marcos Quiñones-Grueiro OrestesLlanes-Santiago Department ofAutomation andComputing Department ofAutomation andComputing UniversidadTecnológicadeLaHabanaJosé UniversidadTecnológicadeLaHabanaJosé AntonioEcheverría AntonioEcheverría Cujae, LaHabana, Cuba Cujae, LaHabana, Cuba AntônioJoséSilva Neto Instituto Politécnico Universidade doEstadodoRiodeJaneiro RiodeJaneiro, Brazil ISSN 2198-4182 ISSN 2198-4190 (electronic) Studies in Systems,DecisionandControl ISBN978-3-030-54737-0 ISBN978-3-030-54738-7 (eBook) https://doi.org/10.1007/978-3-030-54738-7 ©SpringerNatureSwitzerlandAG2021 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained hereinorforanyerrorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregard tojurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland To Sahai Marcos Quiñones-Grueiro To Xiomara, Maria Alejandra and Maria Gabriela Orestes Llanes-Santiago To Gilsineida, Lucas and Luísa Antônio José Silva Neto Preface The present book is intended for use in graduate courses of engineering, applied mathematics and applied computing, where tools from mathematics, data mining, multivariate statistical analysis, computational modeling, and computational intel- ligenceareemployedforsolvingfaultdiagnosisproblemsinmultimodeprocesses. In this book, recent methods are presented and formalized for data-driven fault diagnosis of multimode processes. There are very little books that address the importantissueofmonitoringmultimodesystems.Insomeofthem,theproblemsof dynamic and non-linear behavior as well as the Bayesian approach for fault treat- ment are taken into account. Nonetheless, in all cases, the processes studied only consider steady-state modes. Moreover, the data analysis tools such as clustering methods for multimode processes are not analyzed in depth. The main objective of this book is to formalize, generalize and present in a systematic, organized and clear way the main concepts, approaches and results for the fault diagnosis of multimode continuous Processes by using data-driven methods. The main contributions of the book can then be stated as follows (cid:129) A methodology to design data-driven fault diagnosis methods inspired in the widely known Cross Industry Standard Process for Data Mining (CRISP-DM). (cid:129) Aformaldefinitionofmultimodecontinuousprocessesfromadata-drivenpoint of view together with an algorithm to determine in an automatic fashion if a process can be considered as multimode based on a representative data set. (cid:129) A procedure to apply clustering methods to data acquired from multimode processes such that important features for fault diagnosis can be identified. (cid:129) A methodology to design operating mode identification and fault detection methods for multimode processes. (cid:129) A methodology to design fault cause identification methods based on data-driven classification tools. In the book are also taken into account steady-state modes, but going further it presents fault diagnosis schemes for mixed types of modes (transitions + steady states). Thus, a more realistic approach to fault diagnosis is developed. Moreover, sincemultimodeprocessesaredefinedformallyfromadata-drivenperspectiveand vii viii Preface analgorithmisprovided,thereadercanidentifywhetheraprocessismultimodeor not.Dataanalysistoolsformultimodedataarealsopresented.Therefore,thereader can select the proper tools for developing effective Fault Diagnosis schemes. Threewell-knownbenchmarkproblemsinthescientificliteratureareusedinthe booktoexemplify themethodology andthetools described. Theyare: Continuous StirredTankHeater(CSTH);theHanoiWaterDistributionNetwork(HWDN);and Tennessee Eastman Process (TEP). For the analysis of the experiments developed, several tables and graphics were constructed in order to facilitate the presentation of the results, as well as their interpretation. In addition, the Matlab code for the different methods used in this book is presented in the Appendices. Thefundamentalpre-requisitestoread thisbookareunivariateandmultivariate statisticalmethods.Somebasicsconceptsaboutprogrammingarealsonecessaryfor a better understanding of the different computational tools used in the book. The chapters of this book are summarized as follows: Chapter 1: Fault Diagnosis in Industrial Systems starts with a brief description of model-based methods, and then presents a brief review of the data-driven methods used for fault diagnosis. The fundamental concepts related to the fault diagnosis problem are presented. In the past years, the interest of the scientific community has focused towards new process monitoring problems: dynamic, time-varying and multimode behavior. Therefore, a methodology is presented to design data-driven fault diagnosis methods inspired in the widely known Cross Industry Standard Process for Data Mining (CRISP-DM). This methodologyallowsasystematicdesignoffaultdiagnosismethodsbasedonadata set of the process. Chapter 2: Multimode Continuous Processes presents the characteristics and definitions of multimode continuous processes. It is discussed why they require specific monitoring approaches for achieving good fault diagnosis performance. Aformalmathematicallydefinitionofamultimodecontinuousprocessispresented fromadata-drivenpointofview.Analgorithmbasedonthedefinitionispresented to determine if a process is multimode or not. Three different cases of study of multimode processes are presented and it is illustrated why they are multimode. Chapter3:Clustering forMultimode ContinuousProcesses presentstheuse of clustering methods for labeling the data from multimode continuous processes. Fault diagnosis represents a twofold challenge: the unlabeled data must be char- acterized with respect to the operating modes, and monitoring schemes must be developedbyusingthedataonceithasbeenlabeled.Theformertaskischallenging because of the uncertainty related to the unknown number and types of modes. Therefore, the main clustering solutions applied to multimode processes are dis- cussed according to the types of modes. A clustering procedure is presented to allowthesystematicanalysisofdatasetsfrommultimodeprocesses.Itisillustrated how to use popular clustering methods to identify the features of the three multi- mode continuous processes previously presented in Chap. 2. Chapter 4: Monitoring of Multimode Continuous Processes presents a methodology to design operating mode identification and fault detection methods for multimode processes. A fault can be considered as an unpermitted deviation Preface ix from at least one characteristic property of the system which may interrupt the system’s ability to perform a required function. An important challenge is to dif- ferentiatetheintendedoperationalchangeofmodefromafault.Thecomplexityof thistaskisrelatedtothetypesofmodesofaprocess:steady,non-steadyandmixed. Therefore, three different schemes for operating mode identification and fault detectionofmultimodeprocessesarepresented.Methodsforfaultdetectionduring steady modes and transitions are also described. It is also illustrated how to apply these schemes for the monitoring of the cases of study presented in Chap. 2. Chapter 5: Fault Classification with Data-Driven Methods presents a methodology to design fault cause identification methods based on data-driven classificationtools.Onceafaulthasoccurredinanindustry,themeasurementsare usuallyarchived.However,thedatacorrespondingtotheperiodwherethefaultwas present can be used to determine in the future if the fault occurs again. This is achievedthroughthedesignofdata-drivenclassificationtools.Themaindifficultyof accomplishingsuchtaskinmultimodeprocessesisthatthepatternofafaultmight notbethesamedependingonthemodeduringwhichitoccurs.Fourclassification tools are applied in this chapter for the fault cause identification of the three benchmarkspreviouslystudied.Thechallengesofthefaultcauseidentificationtask are thus exploredin this chapter. Chapter 6: Final remarks highlights the main conclusions from the previous chaptersaswellasthefuturetrendsinmonitoringmultimodecontinuousprocesses with data driven methods. Solving the Fault Diagnosis for Multimode Continuous Processes, through data-drivenmethods,bringstothereaders’informationfromatleastthreedifferent areas: Fault Diagnosis, Unsupervised and Supervised Learning, and Statistical Methods. Hence, the topic of the book covers a broad multi/interdisciplinary sci- entific community. La Habana, Cuba Marcos Quiñones-Grueiro La Habana, Cuba Orestes Llanes-Santiago Nova Friburgo, Brazil Antônio José Silva Neto July 2020 Acknowledgements TheauthorsacknowledgethedecisivesupportprovidedbyCAPES-Foundationfor the Coordination and Improvement of Higher Level Education Personnel, through the project “Computational Modelling for Applications in Engineering and Environment”,ProgramforInstitutionalInternationalizationCAPESPrInt41/2017, Process No. 88887.311757/2018-00. Our gratitude is also due to the Springer Publisher,especiallyitsrepresentationinGermany,toCNPq-NationalCouncilfor ScientificandTechnologicalDevelopmenttoFAPERJ-FoundationCarlosChagas Filho for Research Support of the State of Rio de Janeiro, as well as to the Cuban Ministry of Higher Education MES (Ministerio de Educación Superior), Universidad Tecnológica de la Habana José Antonio Eceheverra, CUJAE and Universidade do Estado de Rio de Janeiro for the publication of this book. We would also like to acknowledge the important contribution of Professor Cristina Verde from Universidad Nacional Autónoma de México. La Habana, Cuba Marcos Quiñones-Grueiro La Habana, Cuba Orestes Llanes-Santiago Nova Friburgo, Brazil Antônio José Silva Neto July, 2020 xi
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