Philippe Weber, Christophe Simon

Benefits of Bayesian Network Models (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Erscheinungstermin: 23. August 2016
• Englisch
• ISBN-13: 9781119347446
• Artikelnr.: 45698510

Autorenporträt

Philippe Weber is Professor at the Engineer School of Sciences and Technologies at the University of Lorraine and at the Research Centre for Automatic Control in Nancy, France. His research concerns dependability and is mainly focused on probabilistic graphical models.

Christophe Simon is Associate Professor at the Research Centre for Automatic Control in Nancy, France. His research concerns dependability and is mainly focused on modeling engineering and uncertainties.

Inhaltsangabe

Foreword by J.-f Aubry ix
Foreword by l Portinale xiii
Acknowledgments xv
Introduction xvii
Part 1 Bayesian Networks 1
Chapter 1 Bayesian Networks: a Modeling Formalism for System Dependability
3
1.1 Probabilistic graphical models: BN 5
1.1.1 BN: a formalism to model dependability 5
1.1.2 Inference mechanism 7
1.2 Reliability and joint probability distributions 8
1.2.1 Multi-state system example 8
1.2.2 Joint distribution 9
1.2.3 Reliability computing 9
1.2.4 Factorization 10
1.3 Discussion and conclusion 14
Chapter 2 Bayesian Network: Modeling Formalism of the Stucture Function of
Boolean Systems 17
2.1 Introduction 17
2.2 BN models in the Boolean case 19
2.2.1 BN model from cut-sets 20
2.2.2 BN model from tie-sets 23
2.2.3 BN model from a top-down approach 25
2.2.4 BN model of a bowtie 26
2.3 Standard Boolean gates CPT 29
2.4 Non-deterministic CPT 31
2.5 Industrial applications 38
2.6 Conclusion 41
Chapter 3 Bayesian Network: Modeling Formalism of the Structure Function of
Multi-State Systems 43
3.1 Introduction 43
3.2 BN models in the multi-state case 43
3.2.1 BN model of multi-state systems from tie-sets 44
3.2.2 BN model of multi-state systems from cut-sets 49
3.2.3 BN model of multi-state systems from functional and dysfunctional
analysis 52
3.3 Non-deterministic CPT 58
3.4 Industrial applications 59
3.5 Conclusion 62
Part 2 Dynamic Bayesian Networks 65
Chapter 4 Dynamic Bayesian Networks: Integrating Environmental and
Operating Constraints in Reliability Computation 67
4.1 Introduction 67
4.2 Component modeled by a DBN 69
4.2.1 DBN model of a MC 70
4.2.2 DBN model of non-homogeneous MC 71
4.2.3 Stochastic process with exogenous constraint 72
4.3 Model of a dynamic multi-state system 75
4.4 Discussion on dependent processes 79
4.5 Conclusion 81
Chapter 5 Dynamic Bayesian Networks: Integrating Reliability Computation in
the Control System 83
5.1 Introduction 83
5.2 Integrating reliability information into the control 84
5.3 Control integrating reliability modeled by DBN 85
5.3.1 Modeling and controlling an over-actuated system 86
5.3.2 Integrating reliability 88
5.4 Application to a drinking water network 90
5.4.1 DBN modeling 91
5.4.2 Results and discussion 92
5.5 Conclusion 95
5.6 Acknowledgments 96
Conclusion 97
Bibliography 101
Index 113