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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, St#65533;phane Buschaert and Sylvie Lesoille. 3. Towards Fourth-generation Nuclear Reactors, Jean-Philippe Nabot, Olivier Gastaldi, Fran#65533;ois Baqu#65533;, K#65533;vin 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, Fran#65533;ois Charpillet, Denis Pomorski and Mireille Bayart. 5. Damage and Forecast Modeling, Anne Barros, Eric Levrat, Mitra Fouladirad, Khanh Le Son, Thomas Ruin, Beno#65533;t Iung, Alexandre Voisin, Maxime Monnin, Antoine Despujols, Emmanuel R#65533;my and Ludovic B#65533;n#65533;trix. 6. Diagnosis of Systems with Multiple Operating Modes, Taha Boukhobza, Fr#65533;d#65533;ric Hamelin, Beno#65533;t Marx, Gilles Mourot, Anca Maria Nagy, Jos#65533; Ragot, Djemal Eddine Chouaib Belkhiat, Kevin Guelton, Dalel Jabri, Noureddine Manamanni, Sinuh#65533; Martinez, Nadhir Messai, Vincent Cocquempot, Assia Hakem, Komi Midzodzi Pekpe, Talel Zouari, Michael Defoort, Mohammed Djemai and J#65533;r#65533;my Van Gorp. 7. Multitask Learning for the Diagnosis of Machine Fleet, Xiyan He, Gilles Mourot, Didier Maquin, Jos#65533; Ragot, Pierre Beauseroy, Andr#65533; Smolarz and Edith Grall-Ma#65533;s. 8. The APPRODYN Project: Dynamic Reliability Approaches to Modeling Critical Systems, Jean-Fran#65533;ois Aubry, Genia Babykina, Nicolae Brinzei, Slimane Medjaher, Anne Barros, Christophe Berenguer, Antoine Grall, Yves Langeron, Danh Ngoc Nguyen, Gilles Deleuze, Beno#65533;te De Saporta, Fran#65533;ois 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, Fran#65533;ois Baqu#65533;, 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-Ma#65533;s 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 Beno#65533;t Iung. 15. Integrating Human and Organizational Factors into the BCD Risk Analysis Model: An Influence Diagram-based approach, Karima Sedki, Philippe Polet and Fr#65533;d#65533;ric Vanderhaegen.

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书名：	Supervision and Safety of Complex Systems
作者：	Matta, Nada; Vandenboomgaerde, Yves; Arlat, Jean
出版时间：	2012-12-17
ISBN：	9781118561744(P-ISBN) ，9781118588017(O-ISBN)
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Cover Supervision and Safety of Complex Systems Title Page Copyright Page Table of Contents Foreword Foreword Introduction PART 1. INDUSTRIAL ISSUES Chapter 1. Safety and Performance of Electricity Production Facilities Chapter 2. Monitoring of Radioactive Waste Disposal Cells in Deep Geological Formation 2.1. Context 2.2. Monitoring of the environment 2.3. Monitoring of geological repository structures 2.4. Conclusion and perspectives Chapter 3. Towards Fourth-generation Nuclear Reactors 3.1. Context 3.2. Surveillance and acoustic detection 3.3. Inspection during operation 3.3.1. The case of acoustic measurements 3.4. Conclusion PART 2. SUPERVISION AND MODELING OF COMPLEX SYSTEMS Chapter 4. Fault-tolerant Data-fusion Method: Application on Platoon Vehicle Localization 4.1. Introduction 4.2. Review 4.3. Bayesian network for data fusion 4.3.1. Bayesian network and Kalman filter 4.4. Localization of a single vehicle: multisensor data fusion with a dynamic Bayesian network 4.4.1. Presentation of the approach developed 4.4.2. Inference in switching Kalman filter 4.4.3. Detailed synopsis of the method based on Bayesian networks 4.4.4. Example of management of multi-hypotheses by a Bayesian network 4.4.5. Illustration of the map localization method using SKF 4.5. Multi-vehicle localization 4.5.1. The problem studied 4.5.2. Communication within the convoy 4.5.3. Sensors used on each vehicle in the convoy 4.5.4. Bayesian network for the localization of a chain of vehicles 4.5.5. Extension of the approach: modeling and localization of a chain of vehicles 4.5.6. The issue with this model 4.5.7. New model for the localization of a chain of vehicles 4.5.8. Proportional commands 4.5.9. Functional analysis of models of the convoy 4.6. Conclusions and perspectives 4.7. Bibliography Chapter 5 Damage and Forecast Modeling 5.1. Introduction 5.1.1. Operational level 5.1.2. Strategic level 5.2. Preliminary study of data 5.2.1. Structure of the database 5.2.2. Performance criterion for the prognostic 5.2.3. Definition of a deterioration indicator 5.3. Construction of the deterioration indicator 5.3.1. Study of the failure space with PCA 5.3.2. Damage indicator defined as a distance 5.4. Estimation of the residual life span RUL)5.4.1. Simple approach based on the life span 5.4.2. Stochastic deterioration model 5.5. Conclusion 5.6. Bibliography Chapter 6. Diagnosis of Systems with Multiple Operating Modes 6.1. Introduction 6.2. Detection of faults for a class of switching systems 6.2.1. Introduction 6.2.2. Structure of the residual generator and observer design 6.2.3. Simulation and results 6.2.4. Conclusions 6.3. Analytical method to obtain a multiple model 6.3.1. Introduction 6.3.2. Setting the problem 6.3.3. Transformation in multiple-model form 6.3.4. Conclusion 6.4. Detection of switching and operating mode recognition without the explicituse of model parameters 6.4.1. Introduction 6.4.2. Diagnosis of SSs with linear modes 6.4.3. Diagnosis of a switching system with uncertain nonlinear modes 6.4.4. Conclusions 6.5. Modeling, observation and monitoring of switching systems: application toa multicellular converter 6.5.1. Introduction 6.5.2. Multicellular converter with two arms or four quadrants 6.5.3. Diagnosing faults in the four quadrant converter 6.5.4. Experimental benchmark for validation 6.6. Bibliography Chapter 7. Multitask Learning for the Diagnosis of Machine Fleet 7.1. Introduction 7.2. Single-task learning of one-class SVM classifier 7.3. Multitask learning of 1-SVM classifiers 7.3.1. Formulation of the problem 7.3.2. Dual problem 7.4. Experimental results 7.4.1. Academic nonlinear example 7.4.2. Analysis of textured images 7.5. Conclusion 7.6. Acknowledgments 7.7. Bibliography Chapter 8. The APPRODYN Project: Dynamic Reliability Approaches to Modeling Critical Systems 8.1.Context and aims 8.1.1.Context 8.1.2. Objectives 8.2. Brief overview of the test case 8.2.1. General remarks 8.2.2. Functional description 8.2.3. Modeling the process 8.2.4. Modeling command logic 8.2.5. Reliability data and state graphs 8.2.6. Ageing 8.2.7. Sensors 8.3. Modeling using a stochastic hybrid automaton approach 8.3.1. Main concepts and references 8.3.2. What is a stochastic hybrid automaton?8.3.3. Structuring and synchronization approach 8.3.4. Modeling the case study 8.3.5. Qualitative and quantitative results 8.3.6. Conclusion and perspectives for the stochastic hybrid automaton approach 8.4. Modeling using piecewise deterministic Markov processes 8.4.1. Principles and references 8.4.2. What is a piecewise deterministic Markov process?8.4.3. Modeling the test case 8.4.4. Modeling the VVP 8.4.5. Modeling CEX 8.4.6. Qualitative and quantitative results 8.4.7. Conclusion and perspectives for the piecewise deterministic Markov processes and simulation approach 8.5. Modeling using stochastic Petri nets 8.5.1. Principles and references 8.5.2. What is a stochastic Petri net?8.5.3. Modeling framework 8.5.4. Qualitative and quantitative results 8.5.5. SPN approach: conclusions and perspectives 8.6. Preliminary conclusion and perspectives 8.7. Bibliography PART 3. CHARACTERIZING BACKGROUND NOISE, IDENTIFYING CHARACTERISTIC SIGNATURES IN TEST CASES AND DETECTING NOISE REACTORS Chapter 9. Aims, Context and Type of Signals Studied Chapter 10. Detection/Classification of Argon and Water Injections into Sodium into an SG of a Fast Neutron Reactor 10.1. Context and aims 10.2. Data 10.3. Online sequential) detection-isolation 10.3.1. Formulating the practical problem 10.3.2. Formulating the statistical problem 10.3.3. Non-recursive approach 10.3.4. Recursive approach 10.3.5. Practical algorithm 10.3.6. Experimental results 10.4. Offline classification non-sequential)10.4.1. Characterization and approach used 10.4.2. Initial characterization 10.4.3. Effective features 10.4.4. Classification 10.4.5. Performance evaluation 10.4.6. Experimental results 10.5. Results and comments 10.6. Conclusion 10.7. Bibliography Chapter 11. A Dynamic Learning-based Approach to the Surveillance and Monitoring of Steam Generators in Prototype Fast Reactors 11.1. Introduction 11.2. Proposed method for the surveillance and monitoring of a steam generator 11.2.1. Learning and classification 11.2.2. Detecting the evolution of a class 11.2.3. Adapting a class after validating its evolution and creating a new class 11.2.4. Validating classes 11.2.5. Defining the parameters of the SS-DFKNN method 11.3. Results 11.3.1. Data analysis 11.3.2. Classification results 11.3.3. Designing an automaton to improve classification rates 11.4. Conclusion and perspectives 11.5. Bibliography Chapter 12. SVM Time-Frequency Classification for the Detection of Injection States 12.1. Introduction 12.2. Preliminary examination of the data 12.2.1. Approach 12.2.2. Spectral analysis of the data 12.2.3. Class visualization 12.3. Detection algorithm 12.3.1. SVM implementation 12.3.2. Algorithm calibration 12.4. Role of sensors 12.5. Experimental results 12.6. Bibliography Chapter 13. Time and Frequency Domain Approaches for the Characterization of Injection States 13.1. Introduction 13.1.1. Framework of the study 13.1.2. Processing recordings 13.1.3. Identifying the injection zones 13.1.4. Extraction of “non-injection” zones 13.2. Analyzing the statistical properties of spectral power densities 13.2.1. Methodology 13.2.2. Results 13.2.3. Exploring implementation in a new installation 13.3. Analysis of the filtering characteristics 13.3.1. Estimating filtering characteristics using an AR model 13.3.2. Comparing filtering characteristics 13.3.3. A leak detection algorithm 13.3.4. Conclusions on the autoregressive signal modeling-based approach 13.4. Conclusion on frequential and temporal approaches 13.5. Bibliography PART 4. HUMAN, ORGANIZATIONAL AND ENVIRONMENTAL FACTORS IN RISK ANALYSIS Chapter 14. Risk Analysis and Management in Systems Integrating Technical, Human, Organizational and Environmental Aspects 14.1. Aims of the project 14.2. State of the art 14.2.1. Context of the study 14.2.2. Towards an “integrated” approach to risk: combining several specialist disciplines 14.3. Integrated risk analysis 14.3.1. Concepts 14.3.2. A description of the approach 14.4. Accounting for uncertainty in risk analysis 14.4.1. Different kinds and sources of uncertainty 14.4.2. Frameworks for modeling uncertainty 14.5. Modeling risk for a quantitative assessment of risk 14.5.1. Bayesian networks 14.5.2. Evaluating risk beyond a probabilistic framework 14.6. Conclusions and future perspectives 14.7. Bibliography Chapter 15. Integrating Human and Organizational Factors into the BCD Risk Analysis Model: An Influence Diagram-based Approach 15.1. Introduction 15.2. Introduction of the BCD benefit-cost-deficit) approach 15.3. Analysis model for human actions 15.3.1. Accounting for organizational and human factors 15.3.2. Influence diagrams 15.3.3. Structure and parameters associated with the risk analysis model 15.4. Example application 15.4.1. Description of the case study: industrial printing presses 15.4.2. Presentation of the model for the test case 15.5. Conclusion 15.6. Acknowledgments 15.7. Bibliography Conclusion Bibliography List of Authors Index

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