139,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A large number of scientific publications show the interest in the applications of BN in this field.
Unfortunately, this modeling formalism is not fully accepted in the industry. The questions facing today's engineers are focused on the validity of BN models and the resulting estimates. Indeed, a BN model is not based on a specific semantic in dependability but offers a general formalism for modeling problems under uncertainty.
This book explains the principles of knowledge structuration to ensure a valid BN and DBN model and illustrate the flexibility and efficiency of these representations in dependability, risk analysis and control of multi-state systems and dynamic systems.
Across five chapters, the authors present several modeling methods and industrial applications are referenced for illustration in real industrial contexts.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 147
Veröffentlichungsjahr: 2016
Ähnliche
Table of Contents
Cover
Title
Copyright
Foreword by J.-F. Aubry
Foreword by L. Portinale
Acknowledgments
Introduction
I.1. Problem statement
I.2. Book structure
PART 1: Bayesian Networks
1 Bayesian Networks: a Modeling Formalism for System Dependability
1.1. Probabilistic graphical models: BN
1.2. Reliability and joint probability distributions
1.3. Discussion and conclusion
2 Bayesian Network: Modeling Formalism of the Structure Function of Boolean Systems
2.1. Introduction
2.2. BN models in the Boolean case
2.3. Standard Boolean gates CPT
2.4. Non-deterministic CPT
2.5. Industrial applications
2.6. Conclusion
3 Bayesian Network: Modeling Formalism of the Structure Function of Multi-State Systems
3.1. Introduction
3.2. BN models in the multi-state case
3.3. Non-deterministic CPT
3.4. Industrial applications
3.5. Conclusion
PART 2: Dynamic Bayesian Networks
4 Dynamic Bayesian Networks: Integrating Environmental and Operating Constraints in Reliability Computation
4.1. Introduction
4.2. Component modeled by a DBN
4.3. Model of a dynamic multi-state system
4.4. Discussion on dependent processes
4.5. Conclusion
5 Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System
5.1. Introduction
5.2. Integrating reliability information into the control
5.3. Control integrating reliability modeled by DBN
5.4. Application to a drinking water network
5.5. Conclusion
5.6. Acknowledgments
Conclusion
Bibliography
Index
End User License Agreement
List of Illustrations
1 Bayesian Networks: a Modeling Formalism for System Dependability
Figure 1.1.
Bayesian network model
Figure 1.2.
Multi-state system with three valves
Figure 1.3.
Multi-state three-valve system with two stages
2 Bayesian Network: Modeling Formalism of the Structure Function of Boolean Systems
Figure 2.1.
RBD of the flow distribution system
Figure 2.2.
BN model for three cut-sets
Figure 2.3.
BN model of two minimal cut-sets
Figure 2.4.
BN modeling the two minimal tie-sets
Figure 2.5.
FT of the flow distribution system
Figure 2.6.
BN model of the FT of Figure 2.5
Figure 2.7.
BN model of a bowtie and its barriers
Figure 2.8.
BN model of the 2-out-of-3:G system
Figure 2.9.
BN model of the linear consecutive-2-out-of-5:G system
Figure 2.10.
BN model of the circular consecutive-2-out-of-5:G system
Figure 2.11.
Noisy-OR structures
Figure 2.12.
Leaky Noisy-OR structures
Figure 2.13.
Structuration in organizational level and action phases relating to a bowtie model
Figure 2.14.
RB unified model of the power plant risk
3 Bayesian Network: Modeling Formalism of the Structure Function of Multi-State Systems
Figure 3.1.
BN structured by the minimal multi-state tie-sets
Figure 3.2.
Compact BN structured by minimal tie-sets for a multi-state system
Figure 3.3.
BN based on the minimal cut-sets of a multi-state system
Figure 3.4.
Generic definition of a function and its flows
Figure 3.5.
Generic BN pattern of a function
Figure 3.6.
Functional model of the system
Figure 3.7.
Model of the function (transfer the fluid)
Figure 3.8.
Model of the function (circulate the fluid)
Figure 3.9.
Model of the function (stop the fluid)
Figure 3.10.
BN model mapped from the functional model of the system
Figure 3.11.
BN model of a human safety barrier (HSB)
4 Dynamic Bayesian Networks: Integrating Environmental and Operating Constraints in Reliability Computation
Figure 4.1.
DBN model developed over eight time slices
Figure 4.2.
DBN of a MC
Figure 4.3.
DBN of a non-homogeneous MC
Figure 4.4.
Inference in the DBN of a non-homogeneous MC
Figure 4.5.
DBN model of a MSM
Figure 4.6.
DBN model of an IOHMM
Figure 4.7.
Inference in the DBN model of the IOHMM
Figure 4.8.
Unroll up the DBN model without conditional dependence between components
Figure 4.9.
Unroll up the DBN model with conditional dependence between components
Figure 4.10.
2TBN of a multi-state system
Figure 4.11.
Inference of a 2TBN multi-state model and state probability distribution
Figure 4.12.
Multi-state system and components’ reliability
Figure 4.13.
2TBN model of a multi-state system with largely dependent processes
5 Dynamic Bayesian Networks: Integrating Reliability Computation in the Control System
Figure 5.1.
Control structure of an over-actuated system integrating a reliability model
Figure 5.2.
Control framework of an over-actuated system integrating a DBN reliability model
Figure 5.3.
Part of the Barcelona DWN studied
Figure 5.4.
DBN model of the Barcelona DWN
Figure 5.5.
Actuators and DWN reliability
Figure 5.6.
Simulation of control inputs and weights of the Barcelona DWN
Guide
Cover
Table of Contents
Begin Reading
Pages
C1
iii
iv
v
ix
x
xi
xiii
xiv
xv
xvii
xviii
xix
xx
xxi
xxii
xxiii
1
3
4
5
6
7
8
9
10
11
12
13
14
15
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
65
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
101
102
103
104
105
106
107
108
109
110
111
112
113
114
G1
G2
G3
G4
G5
G6
G7
G8
Systems Dependability Assessment Set
coordinated byJean-Francois Aubry
Volume 2
Benefits of Bayesian Network Models
Philippe Weber
Christophe Simon
First published 2016 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
27-37 St George’s Road
London SW19 4EU
UK
www.iste.co.uk
John Wiley & Sons, Inc.
111 River Street
Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2016
The rights of Philippe Weber and Christophe Simon to be identified as the author of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2016943665
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-84821-992-2
Foreword by J.-F. Aubry
Systems Dependability Assessment is the title of a series of books, of which this is the third. The preface to the first series described the reasons why the authors embarked upon writing these books: in recent decades, they have made significant contributions to recent approaches to the predictive dependability of systems by considering concepts developed in other scientific fields but not yet applied to account of dependability. All these authors belong to the Automatic Control Research Center (CRAN, Centre de Recherches en Automatique de Nancy) of the University of Lorraine, France, a research laboratory whose activities are widely oriented towards the diagnosis, reliability, maintainability and safety of systems, which can be described in one word: dependability.
Assessment must be understood as the set of means, methods and tools to provide quantitative measures of dependability, and in these books we are interested in providing predictive measures by using probabilistic approaches. The first two books were dedicated to methods based on the frequentist knowledge of basic elements of a system and on models describing how the failure of this system depends on those of its components. These models are essentially of state-transition type, such as finite state automata and petri nets, and results were obtained by analytic or simulation approaches according to the level of complexity introduced, and data inputs for these models were probabilistic distributions of elementary events that were derived from strong feedback. This is within the approach called the frequentist or objective of probability theory.
The present book is different as it is based on Bayesian networks. Frequentist and Bayesian approaches to probabilities were both developed at about the same time in the 18th Century, but the first approach culminated in industrial development in the 19th Century, eclipsing the second. The original mathematical formulation of subjective probabilities was made almost simultaneously by Laplace and Bayes, the latter having given his name to the theorem of probability of causes. The Bayesian approach supposes an a priori knowledge, even if only approximate, of an event probability. From the knowledge of the a priori probabilities of the event and of its cause, the Bayes formula gives an a posteriori probability of the event, its likelihood function somehow describing the causal dependency. It is, in fact, a means of improving the knowledge of the event probability.
Bayes theorem can be materialized by a causal decision tree and extended to represent chained causality in a system. This is the base of Bayesian networks. With the development of computing technologies, the second half of the 20th Century saw the return of Bayesian methods, being an efficient tool to aid decision making in an uncertain environment. They provided solutions to problems such as climate change prediction or, more recently, the detection of spam in data communications.
Dependability assessment problems do not escape from Bayesian approaches. CRAN was among the first to promote these techniques, proposing methods and tools in association with industrial users and developers. Original works have been conducted, especially in the dependability of multi-state systems, integration of environmental and operating constraints in reliability computation (dynamic context) and interaction between the dependability and control of systems. The reader will find in this book a clear presentation of all these advances and I do not doubt that he/she can find a substantial benefit.
Jean-François AUBRYProfessor EmeritusLorraine UniversityNancy, France
Foreword by L. Portinale
Probabilistic graphical models and Bayesian belief networks, in particular, have definitely become a reference formalism in dependability modeling and assessment. The graphical structure, together with the compact representation of the joint distribution of the system variables of interest, provides the reliability engineer with a powerful tool at both the modeling and analytical levels.
The dependency structure, induced by the graph component of the formalism, allows the modeler to make explicit a set of reasonable independence assumptions that may lead to huge simplification at the computational level, as well as with respect to the problem of probability elicitation, without compromising the suitability of the model produced to the actual real-world application.
Standard dependability models usually fit into two categories: 1) combinatorial models (as fault trees or reliability block diagrams) – they determine the occurrence of an undesired event through a combinatorial composition of sub-events; this class of model is very easy to analyze, but it cannot model situations involving complex dependencies among system components and sub-systems; 2) state–space models (such as Markov chains or petri nets) – they allow complex interactions among system parts to be modeled, but they may incur the “state-explosion” problem; this usually means that the analysis has to be performed by considering the cross-product of the system variables, producing a potentially huge number of states.
Bayesian networks and related models allow for efficient factorization of the set of system states, without the need for an explicit representation of the whole joint distribution; moreover it has the additional advantage of inference algorithms available for the analysis of any a posteriori situation of interest (i.e. evidence can be gathered by a monitoring system and fed into a dependability framework for fault detection and identification). Finally, when time is explicitly taken into account, models such as dynamic Bayesian networks result in a factored representation of a Markov chain, providing a framework with the modeling advantages of state–space models, without the drawbacks at the analytical level.
The present book, written by some of the most respected researchers and practitioners in the field, provides a comprehensive presentation and analysis of the probabilistic graphical model approach to dependability, providing a view of the different facets involved in real-world dependability applications: system reliability, maintenance and risk evaluation. The main objective is to devise a principled approach to the modeling of complex dependable systems, with the aim of supporting decisions in an uncertain and evolving setting. This supports and promotes Bayesian networks and probabilistic graphical models as some of the most relevant and important formalisms in modern dependability analysis.
Luigi PORTINALEComputer Science Institute (DiSIT)University of Eastern PiedmontAlessandria, Italy
Acknowledgments
It is not easy to thank all those who have participated in and contributed to the research mentioned in this book.
I want to thank my scientific mentors Professor Benoît Iung and Professor Didier Theilliol, who have guided and supported my activity during all these years we have worked together. Special thanks to Christophe Simon, my co-author, my scientific partner and friend for all our joint contributions.
I thank my industry partners, Carole Duval (EDF), Paul Munteanu and Lionel Jouffe (Bayesia) for their trust and the various joint projects.
I cannot conclude the acknowledgments without extending my warmest thanks to my family, especially my wife Carole, who preserved the equilibrium in my life, and to my children Loïc and Manon, who have filled my family life with happiness.
Philippe WEBERJune 2016
