Table Of ContentStudies 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
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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
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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
