Supervision and Safety of Complex Systems

Overview

This book presents results of projects carried out by both scientific and industry researchers into the techniques to help in maintenance, control, supervision and security of systems, taking into account the technical environmental and human factors.
This work is supported by the Scientific Group GIS 3SGS. It is a collaborative work from 13 partners (academic and industrial) who have come together to deal with security problems. The problems and techniques discussed mainly ...

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Overview

This book presents results of projects carried out by both scientific and industry researchers into the techniques to help in maintenance, control, supervision and security of systems, taking into account the technical environmental and human factors.
This work is supported by the Scientific Group GIS 3SGS. It is a collaborative work from 13 partners (academic and industrial) who have come together to deal with security problems. The problems and techniques discussed mainly focus on stochastic and dynamic modeling, maintenance, forecasting, diagnosis, reliability, performance, organizational, human and environmental factors, uncertainty and experience feedback.

Part 1. Industrial Issues
1. Safety and Performance of Electricity Production Facilities, Gilles Deleuze, Jean Primet, Philippe Klein, Carole Duval and Antoine Despujols.
2. Monitoring of Radioactive Waste Disposal Cells in Deep Geological Formation, Stéphane Buschaert and Sylvie Lesoille.
3. Towards Fourth-generation Nuclear Reactors, Jean-Philippe Nabot, Olivier Gastaldi, François Baqué, Kévin Paumel and Jean-Philippe Jeannot.
Part 2. Supervison and Modeling of Complex Systems
4. Fault-tolerant Data-fusion Method: Application on Platoon Vehicle Localization, Maan El Badaoui El Najiar, Cherif Smaili, François Charpillet, Denis Pomorski and Mireille Bayart.
5. Damage and Forecast Modeling, Anne Barros, Eric Levrat, Mitra Fouladirad, Khanh Le Son, Thomas Ruin, Benoît Iung, Alexandre Voisin, Maxime Monnin, Antoine Despujols, Emmanuel Rémy and Ludovic Bénétrix.
6. Diagnosis of Systems with Multiple Operating Modes, Taha Boukhobza, Frédéric Hamelin, Benoît Marx, Gilles Mourot, Anca Maria Nagy, José Ragot, Djemal Eddine Chouaib Belkhiat, Kevin Guelton, Dalel Jabri, Noureddine Manamanni, Sinuhé Martinez, Nadhir Messai, Vincent Cocquempot, Assia Hakem, Komi Midzodzi Pekpe, Talel Zouari, Michael Defoort, Mohammed Djemai and Jérémy Van Gorp.
7. Multitask Learning for the Diagnosis of Machine Fleet, Xiyan He, Gilles Mourot, Didier Maquin, José Ragot, Pierre Beauseroy, André Smolarz and Edith Grall-Maës.
8. The APPRODYN Project: Dynamic Reliability Approaches to Modeling Critical Systems, Jean-François Aubry, Genia Babykina, Nicolae Brinzei, Slimane Medjaher, Anne Barros, Christophe Berenguer, Antoine Grall, Yves Langeron, Danh Ngoc Nguyen, Gilles Deleuze, Benoîte De Saporta, François Dufour and Huilong Zhang.
Part 3. Characterizing Background Noise, Identifying Characteristic Signatures in Test Cases and Detecting Noise Reactors
9. Aims, Context and Type of Signals Studied, François Baqué, Olivier Descombin, Olivier Gastaldi and Yves Vandenboomgaerde.
10. Detection/Classification of Argon and Water Injections into Sodium into an SG of a Fast Neutron Reactor, Pierre Beauseroy, Edith Grall-Maës and Igor Nikiforov.
11. A Dynamic Learning-based Approach to the Surveillance and Monitoring of Steam Generators in Prototype Fast Reactors, Laurent Hartert, Moamar Sayed-Mouchaweh and Danielle Nuzillard.
12. SVM Time-Frequency Classification for the Detection of Injection States, Simon Henrot, El-Hadi Djermoune and David Brie.
13. Time and Frequency Domain Approaches for the Characterization of Injection States, Jean-Philippe Cassar and Komi Midzodzi Pekpe.
Part 4. Human, Organizational and Environmental Factors in Risk Analysis
14. Risk Analysis and Management in Systems Integrating Technical, Human, Organizational and Environmental Aspects, Geoffrey Fallet-Fidry, Carole Duval, Christophe Simon, Eric Levrat, Philippe Weber and Benoît Iung.
15. Integrating Human and Organizational Factors into the BCD Risk Analysis Model: An Influence Diagram-based approach, Karima Sedki, Philippe Polet and Frédéric Vanderhaegen.

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Editorial Reviews

From the Publisher
"This is therefore a helpful contribution to the literature and a book that those in the nuclear industry in particular should have on their bookshelves."  (Industrial Systems and Control Ltd, 1 March 2013)
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Product Details

  • ISBN-13: 9781848214132
  • Publisher: Wiley
  • Publication date: 10/9/2012
  • Series: ISTE Series, #720
  • Edition number: 1
  • Pages: 384
  • Product dimensions: 6.00 (w) x 9.20 (h) x 1.00 (d)

Table of Contents

Foreword Eric Besson xiii

Foreword Christian Lerminiaux xv

Introduction Yves Vandenboomgaerde Christian Lerminiaux Nada Matta xvii

Part 1 Industrial Issues 1

Chapter 1 Safety and Performance of Electricity Production Facilities Gilles Deleuze Jean Primet Philippe Klein Carole Duval Antoine Despujols 3

Chapter 2 Monitoring of Radioactive Waste Disposal Cells in Deep Geological Formation Stéphane Buschaert Sylvie Lesoille 7

2.1 Context 7

2.2 Monitoring of the environment 8

2.3 Monitoring of geological repository structures 9

2.4 Conclusion and perspectives 12

Chapter 3 Towards Fourth-generation Nuclear Reactors Jean-Philippe Nabot Olivier Gastaldi François Baqué Kévin Paumel Jean-Philippe Jeannot 13

3.1 Context 13

3.2 Surveillance and acoustic detection 15

3.3 Inspection during operation 17

3.3.1 The case of acoustic measurements 18

3.4 Conclusion 18

Part 2 Supervison and Modeling of Complex Systems 19

Chapter 4 Fault-tolerant Data-fusion Method: Application on Platoon Vehicle Localization Maan El Badaoui El Najiar Cherif Smaili François Charpillet Denis Pomorski Mireille Bayart 21

4.1 Introduction 21

4.2 Review 22

4.3 Bayesian network for data fusion 25

4.3.1 Bayesian network and Kalman filter 26

4.4 Localization of a single vehicle: multisensor data fusion with a dynamic Bayesian network 28

4.4.1 Presentation of the approach developed 30

4.4.2 Inference in switching Kalman filter 33

4.4.3 Detailed synopsis of the method based on Bayesian networks 35

4.4.4 Example of management of multi-hypotheses by a Bayesian network 36

4.4.5 Illustration of the map localization method using SKF 38

4.5 Multi-vehicle localization 42

4.5.1 The problem studied 42

4.5.2 Communication within the convoy 43

4.5.3 Sensors used on each vehicle in the convoy 43

4.5.4 Bayesian network for the localization of a chain of vehicles 45

4.5.5 Extension of the approach: modeling and localization of a chain of vehicles 45

4.5.6 The issue with this model 48

4.5.7 New model for the localization of a chain of vehicles 48

4.5.8 Proportional commands 50

4.5.9 Functional analysis of models of the convoy 54

4.6 Conclusions and perspectives 55

4.7 Bibliography 57

Chapter 5 Damage and Forecast Modeling Anne Barros Eric Levrat Mitra Fouladirad Khanh Le Son Thomas Ruin Benoît Iung Alexandre Voisin Maxime Monnin Antoine Despujols Emmanuel Rémy Ludovic Bénétrix 61

5.1 Introduction 61

5.1.1 Operational level 62

5.1.2 Strategic level 62

5.2 Preliminary study of data 63

5.2.1 Structure of the database 63

5.2.2 Performance criterion for the prognostic 63

5.2.3 Definition of a deterioration indicator 64

5.3 Construction of the deterioration indicator 65

5.3.1 Study of the failure space with PCA 65

5.3.2 Damage indicator defined as a distance 66

5.4 Estimation of the residual life span (RUL) 68

5.4.1 Simple approach based on the life span 68

5.4.2 Stochastic deterioration model 70

5.5 Conclusion 72

5.6 Bibliography 73

Chapter 6 Diagnosis of Systems with Multiple Operating Modes Taha Boukhobza Frédéric Hamelin Benoît Marx Gilles Mourot Anca Maria Nagy José Ragot Djemal Eddine Chouaib Belkhiat Kevin Guelton Dalel Jabri Noureddine Manamanni Sinuhé Martinez Nadhir Messai Vincent Cocquempot Assia Hakem Komi Midzodzi Pekpe Talel Zouari Michael Defoort Mohammed Djemai Jérémy Van Gorp 75

6.1 Introduction 75

6.2 Detection of faults for a class of switching systems 77

6.2.1 Introduction 77

6.2.2 Structure of the residual generator and observer design 78

6.2.3 Simulation and results 81

6.2.4 Conclusions 83

6.3 Analytical method to obtain a multiple model 83

6.3.1 Introduction 83

6.3.2 Setting the problem 84

6.3.3 Transformation in multiple-model form 85

6.3.4 Conclusion 89

6.4 Detection of switching and operating mode recognition without the explicit use of model parameters 89

6.4.1 Introduction 89

6.4.2 Diagnosis of SSs with linear modes 90

6.4.3 Diagnosis of a switching system with uncertain nonlinear modes 96

6.4.4 Conclusions 100

6.5 Modeling, observation and monitoring of switching systems: application to a multicellular converter 100

6.5.1 Introduction 100

6.5.2 Multicellular converter with two arms or four quadrants 101

6.5.3 Diagnosing faults in the four quadrant converter 102

6.5.4 Experimental benchmark for validation 107

6.6 Bibliography 109

Chapter 7 Multitask Learning for the Diagnosis of Machine Fleet Xiyan He Gilles Mourot Didier Maquin José Ragot Pierre Beauseroy André Smolarz Edith Grall-Maës 115

7.1 Introduction 115

7.2 Single-task learning of one-class SVM classifier 119

7.3 Multitask learning of 1-SVM classifiers 122

7.3.1 Formulation of the problem 122

7.3.2 Dual problem 124

7.4 Experimental results 125

7.4.1 Academic nonlinear example 125

7.4.2 Analysis of textured images 126

7.5 Conclusion 136

7.6 Acknowledgements 136

7.7 Bibliography 136

Chapter 8 The Approdyn Project: Dynamic Reliability Approaches to Modeling Critical Systems Jean-François Aubry Genia Babykina Nicolae Brinzei Slimane Medjaher Anne Barros Christophe Berenguer Antoine Grall Yves Langeron Danh Ngoc Nguyen Gilles Deleuze Benoîte De Saporta François Dufour Huilong Zhang 141

8.1 Context and aims 141

8.1.1 Context 141

8.1.2 Objectives 142

8.2 Brief overview of the test case 143

8.2.1 General remarks 143

8.2.2 Functional description 143

8.2.3 Modeling the process 144

8.2.4 Modeling command logic 145

8.2.5 Reliability data and state graphs 146

8.2.6 Ageing 146

8.2.7 Sensors 147

8.3 Modeling using a stochastic hybrid automaton approach 147

8.3.1 Main concepts and references 147

8.3.2 What is a stochastic hybrid automaton? 148

8.3.3 Structuring and synchronization approach 150

8.3.4 Modeling the case study 151

8.3.5 Qualitative and quantitative results 154

8.3.6 Conclusion and perspectives for the stochastic hybrid automaton approach 156

8.4 Modeling using piecewise deterministic Markov processes 157

8.4.1 Principles and references 157

8.4.2 What is a piecewise deterministic Markov process? 158

8.4.3 Modeling the test case 159

8.4.4 Modeling the VVP 163

8.4.5 Modeling CEX 163

8.4.6 Qualitative and quantitative results 164

8.4.7 Conclusion and perspectives for the piecewise deterministic Markov processes and simulation approach 167

8.5 Modeling using stochastic Petri nets 168

8.5.1 Principles and references 168

8.5.2 What is a stochastic Petri net? 168

8.5.3 Modeling framework 170

8.5.4 Qualitative and quantitative results 173

8.5.5 SPN approach: conclusion and perspectives 176

8.6 Preliminary conclusion and perspectives 177

8.7 Bibliography 177

Part 3 Characterizing Background Noise, Identifying Characteristic Signatures in Test Cases and Detecting Noise Reactors 181

Chapter 9 Aims, Context and Type of Signals Studied François Baqué Olivier Descombin Olivier Gastaldi Yves Vandenboomgaerde 183

Chapter 10 Detection/Classification of Argon and Water Injections into Sodium into an SG of a Fast Neutron Reactor Pierre Beauseroy Edith Grall-Maës Igor Nikiforov 191

10.1 Context and aims 191

10.2 Data 192

10.3 Online (sequential) detection-isolation 193

10.3.1 Formulating the practical problem 193

10.3.2 Formulating the statistical problem 195

10.3.3 Non-recursive approach 196

10.3.4 Recursive approach 198

10.3.5 Practical algorithm 200

10.3.6 Experimental results 200

10.4 Offline classification (non-sequential) 201

10.4.1 Characterization and approach used 201

10.4.2 Initial characterization 202

10.4.3 Effective features 204

10.4.4 Classification 205

10.4.5 Performance evaluation 207

10.4.6 Experimental results 208

10.5 Results and comments 209

10.6 Conclusion 209

10.7 Bibliography 210

Chapter 11 A Dynamic Learning-based Approach to the Surveillance and Monitoring of Steam Generators in Prototype Fast Reactors Laurent Hartert Moamar Sayed-Mouchaweh Danielle Nuzillard 213

11.1 Introduction 214

11.2 Proposed method for the surveillance and monitoring of a steam generator 215

11.2.1 Learning and classification 216

11.2.2 Detecting the evolution of a class 217

11.2.3 Adapting a class after validating its evolution and creating a new class 218

11.2.4 Validating classes 219

11.2.5 Defining the parameters of the SS-DFKNN method 221

11.3 Results 222

11.3.1 Data analysis 222

11.3.2 Classification results 224

11.3.3 Designing an automaton to improve classification rates 226

11.4 Conclusion and perspectives 227

11.5 Bibliography 228

Chapter 12 SVM Time-Frequency Classification for the Detection of Injection States Simon Henrot El-Hadi Djermoune David Brie 231

12.1 Introduction 231

12.2 Preliminary examination of the data 232

12.2.1 Approach 232

12.2.2 Spectral analysis of the data 232

12.2.3 Class visualization 235

12.3 Detection algorithm 236

12.3.1 SVM implementation 236

12.3.2 Algorithm calibration 238

12.4 Role of sensors 242

12.5 Experimental results 242

12.6 Bibliography 245

Chapter 13 Time and Frequency Domain Approaches for the Characterization of Injection States Jean-Philippe Cassar Komi Midzodzi Pekpe 247

13.1 Introduction 247

13.1.1 Framework of the study 247

13.1.2 Processing recordings 248

13.1.3 Identifying the injection zones 248

13.1.4 Extraction of "non-injection" zones 248

13.2 Analyzing the statistical properties of spectral power densities 249

13.2.1 Methodology 249

13.2.2 Results 250

13.2.3 Exploring implementation in a new installation 254

13.3 Analysis of the filtering characteristics 256

13.3.1 Estimating filtering characteristics using an AR model 256

13.3.2 Comparing filtering characteristics 257

13.3.3 A leak detection algorithm 260

13.3.4 Conclusions on the autoregressive signal modeling-based approach 261

13.4 Conclusion on frequential and temporal approaches 262

13.5 Bibliography 263

Part 4 Human, Organizational and Environmental Factors in Risk Analysis 265

Chapter 14 Risk Analysis and Management in Systems Integrating Technical, Human, Organizational and Environmental Aspects Geoffrey Fallet-Fidry Carole Duval Christophe Simon Eric Levrat Philippe Weber Benoît Iung 267

14.1 Aims of the porject 267

14.2 State of the art 268

14.2.1 Context of the study 268

14.2.2 Towards an "integrated" approach to risk: combining several specialist disciplines 270

14.3 Integrated risk analysis 272

14.3.1 Concepts 272

14.3.2 A description of the approach 273

14.4 Accounting for uncertainty in risk analysis 278

14.4.1 Different kinds and sources of uncertainty 278

14.4.2 Frameworks for modeling uncertainty 280

14.5 Modeling risk for a quantitative assessment of risk 284

14.5.1 Bayesian networks 284

14.5.2 Evaluating risk beyond a probabilistic framework 285

14.6 Conclusions and future perspectives 286

14.7 Bibliography 286

Chapter 15 Integrating Human and Organizational Factors into the BCD Risk Analysis Model: An Influence Diagram-based approach Karima Sedki Philippe Polet Frédéric Vanderhaegen 293

15.1 Introduction 293

15.2 Introduction of the BCD (benefit-cost-deficit) approach 295

15.3 Analysis model for human actions 299

15.3.1 Accounting for organizational and human factors 300

15.3.2 Influence diagrams 301

15.3.3 Structure and parameters associated with the risk analysis model 302

15.4 Example application 305

15.4.1 Description of the case study: industrial printing presses 305

15.4.2 Presentation of the model for the test case 306

15.5 Conclusion 314

15.6 Acknowledgements 314

15.7 Bibliography 314

Conclusion Jean Arlat Nada Matta 317

Bibliography 325

List of Authors 327

Index 333

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