Capturing Connectivity and Causality in Complex Industrial Processes

99 Pages·2014·4.656 MB·English

by  Fan Yang, Ping Duan, Sirish L. Shah, Tongwen Chen (auth.)

SPRINGER BRIEFS IN
APPLIED SCIENCES AND TECHNOLOGY
Fan Yang
Ping Duan
Sirish L. Shah
Tongwen Chen
Capturing
Connectivity
and Causality
in Complex
Industrial
Processes
123
SpringerBriefs in Applied Sciences
and Technology
For furthervolumes:
http://www.springer.com/series/8884
Fan Yang Ping Duan Sirish L. Shah
• •
Tongwen Chen
Capturing Connectivity
and Causality in Complex
Industrial Processes
123
Fan Yang Sirish L.Shah
TsinghuaLaboratory forInformation Department of Chemical andMaterials
Scienceand Technology Engineering
Department ofAutomation Universityof Alberta
TsinghuaUniversity Edmonton, AB
Beijing Canada
China
Ping Duan
Tongwen Chen
Department of Electricaland Computer
Engineering
Universityof Alberta
Edmonton, AB
Canada
ISSN 2191-530X ISSN 2191-5318 (electronic)
ISBN 978-3-319-05379-0 ISBN 978-3-319-05380-6 (eBook)
DOI 10.1007/978-3-319-05380-6
Springer ChamHeidelberg New YorkDordrecht London
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(cid:2)TheAuthor(s)2014
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Preface
Large-scale complex systems, such as modern industrial processes, biological
systems,andsocialnetworks,areinterconnectedbydifferentunitsorelements;the
system behavior is determined by the inter-relationship between every pair of the
elements as well as the local dynamics within each element. It is essential to
identify such inter-relationship, namely connectivity and causality, in order to
analyze influence mechanisms, structural properties, and overall dynamic
behavior.
In the control and automation community, connectivity and causality play a
vital role in modeling and analysis, especially for fault detection and hazard
analysis,becauseanabnormalitycaneasilypropagatewithinandbetweenprocess
units due to material and information flow paths. Thus the problem of fault
detectionandisolationforindustrialprocessesisconcernedwithdeterminationof
root causes and fault propagation. Connectivity and causality, as the key features
of process description, can be captured in two ways:
1. From process knowledge: Structural modeling based on first principles
structural models can be merged with adjacency/reachability matrices or
topology models obtained from process flow-sheets described in standard
formats.
2. From process data: Cross-correlation analysis, Granger causality and its
extensions, frequency domain methods, information-theoretic methods, and
Bayesian networks can be used to identify pairwise relationship and network
topology.
Thesemethodsrelyonthenotionofinformationfusion,wherebyvarioustypes
ofprocessoperatingdataarecombinedwithqualitativeprocessknowledgetogive
a holistic picture of the system.
Inthisbook,weshallgiveanexhaustiveoverviewofconceptsanddescriptions
ofconnectivityandcausalityincomplexprocessesandatutorialguidetoclassical
and recent research results on detection of connectivity and causality illustrated
withexampleapplications.Astudyofthefusionofdifferentinformationresources
for obtaining an acceptable process topology is also introduced.
vii
viii Preface
Some details are omitted in this book due to space constraints. Interested
readers should refer to the related literature. For questions, comments, and
suggestions, please write to Fan Yang at [email protected].
December 2013 Fan Yang
Ping Duan
Sirish L. Shah
Tongwen Chen
Acknowledgments
This book would not have been possible without the help from many people.
Fan Yang expresses his sincere thanks to Prof. Deyun Xiao at Tsinghua
University (Beijing, China) for his long-term guidance and help. Fan Yang’s
student,Mr.CenGuo,kindlyhelpedineditingsomepartofthedraft.FanYangis
grateful for financial support from the National Natural Science Foundation of
China (No. 60904044), the National High-tech R&D Program of China
(No.2013AA040702)andtheFoundationofKeyLaboratoryofAdvancedProcess
Control for Light Industry (Jiangnan University), Ministry of Education, China.
PingDuanisthankfultoProf.NinaF.ThornhillfromImperialCollegeLondon,
UK, for comments and suggestions on her research work on direct causality
analysis, and to Dr. Yuri Shardt for help with the three-tank experiment.
Sirish L. Shah thanks his students, research fellows, and his grandchildren
(Maya, Keshav, Anuva, and Avni), all of whom have been the main sources or
CAUSES of his enriched life.
TongwenChenthankshispastpost-doctoralfellowsandgraduatestudentswho
collaborated on related research topics on advanced alarm monitoring, including
Iman Izadi, Jiandong Wang, Yue Cheng, Naseeb Adnan, Kabir Ahmed, and
Md Shahedul Amin. Tongwen Chen’s research in this area was supported by a
strategic project grant from the Natural Sciences and Engineering Research
CouncilofCanadaentitled‘‘DevelopmentofanAdvancedTechnologyforAlarm
Analysis and Design’’ (2009–2012).
AcknowledgmentisgiventotheInstituteofElectricalandElectronicEngineers
(IEEE) to reproduce material from the following papers:
(cid:2)2013IEEE.Reprinted,withpermission,fromDuanP,YangF,ChenT,ShahSL
(2013) Direct causality detection via the transfer entropy approach. IEEE
Transactions on Control Systems Technology, 21(6):2052–2066 (as related to
material in Chap. 5)
(cid:2)2009 IEEE. Reprinted, with permission, from Yang F, Shah SL, Xiao D (2009)
SDG model-based analysis of fault propagation in control systems. In: Proc.
2009 Canadian Conference on Electrical and Computer Engineering, St John’s,
NL, Canada, pp 1152–1157 (as related to material in Chap. 4)
ix
x Acknowledgments
Acknowledgment is given to International Federation of Automatic Control
(IFAC) to reproduce material from the following paper:
(cid:2)2010 IFAC. Originally published in IFAC-PapersOnLine. Reprinted, with per-
mission, from Yang F, Shah SL, Xiao D (2010) signed directed graph (SDG)-
based process description and fault propagation analysis for a tailings pumping
process.In:Proceedingsof13thSymposiumonAutomationinMining,Mineral
and Metal Processing, Cape Town, South Africa, pp 50–55 (as related to
material in Chap. 6)
AcknowledgmentisgiventoElseviertoreproducematerialfromthefollowing
paper:
(cid:2)2006 Elsevier. Reprinted, with permission, from Yim SY, Ananthakumar HG,
Benabbas L, Horch A, Drath R, Thornhill N (2006) Using process topology in
plant-wide control loop performance assessment. Computers and Chemical
Engineering, 31(2):86–99 (Fig. 4.1 in Chap. 4)
Acknowledgment is given to University of Zielona Góra and Lubuskie
Scientific Society to reproduce material from the following paper:
(cid:2)2011 University of Zielona Góra and Lubuskie Scientific Society. Reprinted,
with permission, from Yang F, Shah SL, Xiao D (2011) Signed directed graph-
based modeling and its validation from process knowledge and process data.
International Journal of Applied Mathematics and Computer Science,
22(1):41–53 (as related to material in Chaps. 5 and 6)
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Concept of Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Connectivity, Correlation, and Causality. . . . . . . . . . . . . . . . . . 4
1.3 Preview of Chapters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Examples of Applications for Connectivity
and Causality Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 Topology Modeling and Closed-Loop Identification . . . . . . . . . 7
2.2 Root Cause Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Risk Analysis: HAZOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Consequential Alarm Identification . . . . . . . . . . . . . . . . . . . . . 9
2.5 Plant-Wide Control Structure Design. . . . . . . . . . . . . . . . . . . . 10
2.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 Description of Connectivity and Causality. . . . . . . . . . . . . . . . . . . 13
3.1 Description of Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1.1 Adjacency Matrices. . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.1.2 Digraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.1.3 Semantic Web Description. . . . . . . . . . . . . . . . . . . . . . 16
3.2 Description of Causality. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2.1 Structural Equation Models . . . . . . . . . . . . . . . . . . . . . 18
3.2.2 Matrices and Digraphs. . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2.3 Matrix Layout Plots . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
xi