You are previewing Supervision and Safety of Complex Systems.
O'Reilly logo
Supervision and Safety of Complex Systems

Book Description

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.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Foreword
  5. Introduction
  6. Part 1. Industrial Issues
    1. Chapter 1. Safety and Performance of Electricity Production Facilities
    2. Chapter 2. Monitoring of Radioactive Waste Disposal Cells in Deep Geological Formation
      1. 2.1. Context
      2. 2.2. Monitoring of the environment
      3. 2.3. Monitoring of geological repository structures
      4. 2.4. Conclusion and perspectives
    3. Chapter 3. Towards Fourth-generation Nuclear Reactors
      1. 3.1. Context
      2. 3.2. Surveillance and acoustic detection
      3. 3.3. Inspection during operation
        1. 3.3.1. The case of acoustic measurements
      4. 3.4. Conclusion
  7. Part 2. Supervison and Modeling of Complex Systems
    1. Chapter 4. Fault-tolerant Data-fusion Method: Application on Platoon Vehicle Localization
      1. 4.1. Introduction
      2. 4.2. Review
      3. 4.3. Bayesian network for data fusion
        1. 4.3.1. Bayesian network and Kalman filter
          1. 4.3.1.1. The choice of use of Bayesian networks
      4. 4.4. Localization of a single vehicle: multisensor data fusion with a dynamic Bayesian network
        1. 4.4.1. Presentation of the approach developed
        2. 4.4.2. Inference in switching Kalman filter
        3. 4.4.3. Detailed synopsis of the method based on Bayesian networks
        4. 4.4.4. Example of management of multi-hypotheses by a Bayesian network
        5. 4.4.5. Illustration of the map localization method using SKF
          1. 4.4.5.1. First ambiguous situation: the case of parallel roads
          2. 4.4.5.2. Second ambiguous situation: the case of a road junction
      5. 4.5. Multi-vehicle localization
        1. 4.5.1. The problem studied
        2. 4.5.2. Communication within the convoy
        3. 4.5.3. Sensors used on each vehicle in the convoy
        4. 4.5.4. Bayesian network for the localization of a chain of vehicles
        5. 4.5.5. Extension of the approach: modeling and localization of a chain of vehicles
        6. 4.5.6. The issue with this model
        7. 4.5.7. New model for the localization of a chain of vehicles
        8. 4.5.8. Proportional commands
        9. 4.5.9. Functional analysis of models of the convoy
      6. 4.6. Conclusions and perspectives
      7. 4.7. Bibliography
    2. Chapter 5. Damage and Forecast Modeling
      1. 5.1. Introduction
        1. 5.1.1. Operational level
        2. 5.1.2. Strategic level
      2. 5.2. Preliminary study of data
        1. 5.2.1. Structure of the database
        2. 5.2.2. Performance criterion for the prognostic
        3. 5.2.3. Definition of a deterioration indicator
      3. 5.3. Construction of the deterioration indicator
        1. 5.3.1. Study of the failure space with PCA
        2. 5.3.2. Damage indicator defined as a distance
      4. 5.4. Estimation of the residual life span (RUL)
        1. 5.4.1. Simple approach based on the life span
        2. 5.4.2. Stochastic deterioration model
          1. 5.4.2.1. Estimation of parameters of Wiener process
          2. 5.4.2.2. RUL estimation by simulation of a Wiener process
      5. 5.5. Conclusion
      6. 5.6. Bibliography
    3. Chapter 6. Diagnosis of Systems with Multiple Operating Modes
      1. 6.1. Introduction
      2. 6.2. Detection of faults for a class of switching systems
        1. 6.2.1. Introduction
        2. 6.2.2. Structure of the residual generator and observer design
          1. 6.2.2.1. Observer design for fault detection
          2. 6.2.2.2. Robustness with respect to unknown inputs and sensitivity to faults
        3. 6.2.3. Simulation and results
        4. 6.2.4. Conclusions
      3. 6.3. Analytical method to obtain a multiple model
        1. 6.3.1. Introduction
        2. 6.3.2. Setting the problem
        3. 6.3.3. Transformation in multiple-model form
          1. 6.3.3.1. General method
          2. 6.3.3.2. Criteria for the choice of quasi-LPV form
        4. 6.3.4. Conclusion
      4. 6.4. Detection of switching and operating mode recognition without the explicit use of model parameters
        1. 6.4.1. Introduction
        2. 6.4.2. Diagnosis of SSs with linear modes
          1. 6.4.2.1. Formulation of the problem
          2. 6.4.2.2. Residual calculation for the estimation of switching time
          3. 6.4.2.3. Sensitivity of the residual to changes in mode
          4. 6.4.2.4. Residual based on data for recognizing the current mode
          5. 6.4.2.5. Tuning the method
          6. 6.4.2.6. Summary of the method
        3. 6.4.3. Diagnosis of a switching system with uncertain nonlinear modes
          1. 6.4.3.1. Model of a switching system with nonlinear modes
          2. 6.4.3.2. Residual generation
        4. 6.4.4. Conclusions
      5. 6.5. Modeling, observation and monitoring of switching systems: application to a multicellular converter
        1. 6.5.1. Introduction
        2. 6.5.2. Multicellular converter with two arms or four quadrants
        3. 6.5.3. Diagnosing faults in the four quadrant converter
          1. 6.5.3.1. Overview of the converter faults
          2. 6.5.3.2. Diagnosis based on an observer
          3. 6.5.3.3. Simulation results
        4. 6.5.4. Experimental benchmark for validation
      6. 6.6. Bibliography
    4. Chapter 7. Multitask Learning for the Diagnosis of Machine Fleet
      1. 7.1. Introduction
      2. 7.2. Single-task learning of one-class SVM classifier
      3. 7.3. Multitask learning of 1-SVM classifiers
        1. 7.3.1. Formulation of the problem
        2. 7.3.2. Dual problem
      4. 7.4. Experimental results
        1. 7.4.1. Academic nonlinear example
        2. 7.4.2. Analysis of textured images
      5. 7.5. Conclusion
      6. 7.6. Acknowledgements
      7. 7.7. Bibliography
    5. Chapter 8. The APPRODYN Project: Dynamic Reliability Approaches to Modeling Critical Systems
      1. 8.1. Context and aims
        1. 8.1.1. Context
        2. 8.1.2. Objectives
      2. 8.2. Brief overview of the test case
        1. 8.2.1. General remarks
        2. 8.2.2. Functional description
        3. 8.2.3. Modeling the process
        4. 8.2.4. Modeling command logic
        5. 8.2.5. Reliability data and state graphs
        6. 8.2.6. Ageing
        7. 8.2.7. Sensors
      3. 8.3. Modeling using a stochastic hybrid automaton approach
        1. 8.3.1. Main concepts and references
        2. 8.3.2. What is a stochastic hybrid automaton?
          1. 8.3.2.1. Definition
        3. 8.3.3. Structuring and synchronization approach
        4. 8.3.4. Modeling the case study
        5. 8.3.5. Qualitative and quantitative results
        6. 8.3.6. Conclusion and perspectives for the stochastic hybrid automaton approach
      4. 8.4. Modeling using piecewise deterministic Markov processes
        1. 8.4.1. Principles and references
        2. 8.4.2. What is a piecewise deterministic Markov process?
        3. 8.4.3. Modeling the test case
        4. 8.4.4. Modeling the VVP
        5. 8.4.5. Modeling CEX
        6. 8.4.6. Qualitative and quantitative results
        7. 8.4.7. Conclusion and perspectives for the piecewise deterministic Markov processes and simulation approach
      5. 8.5. Modeling using stochastic Petri nets
        1. 8.5.1. Principles and references
        2. 8.5.2. What is a stochastic Petri net?
        3. 8.5.3. Modeling framework
          1. 8.5.3.1. Process variables and parameters
          2. 8.5.3.2. Component state variables
          3. 8.5.3.3. Information variables
          4. 8.5.3.4. MOCA-RP
        4. 8.5.4. Qualitative and quantitative results
          1. 8.5.4.1. Initial tests
          2. 8.5.4.2. Real-life tests
        5. 8.5.5. SPN approach: conclusions and perspectives
      6. 8.6. Preliminary conclusion and perspectives
      7. 8.7. Bibliography
  8. Part 3. Characterizing Background Noise, Identifying Characteristic Signatures in Test Cases and Detecting Noise Reactors
    1. Chapter 9. Aims, Context and Type of Signals Studied
    2. Chapter 10. Detection/Classification of Argon and Water Injections into Sodium into an SG of a Fast Neutron Reactor
      1. 10.1. Context and aims
      2. 10.2. Data
      3. 10.3. Online (sequential) detection-isolation
        1. 10.3.1. Formulating the practical problem
        2. 10.3.2. Formulating the statistical problem
        3. 10.3.3. Non-recursive approach
          1. 10.3.3.1. Optimality criterion
          2. 10.3.3.2. Non-recursive test
          3. 10.3.3.3. Non-recursive test performance
        4. 10.3.4. Recursive approach
          1. 10.3.4.1. Optimality criterion
          2. 10.3.4.2. Recursive test
        5. 10.3.5. Practical algorithm
        6. 10.3.6. Experimental results
      4. 10.4. Offline classification (non-sequential)
        1. 10.4.1. Characterization and approach used
        2. 10.4.2. Initial characterization
        3. 10.4.3. Effective features
        4. 10.4.4. Classification
        5. 10.4.5. Performance evaluation
        6. 10.4.6. xperimental results
          1. 10.4.6.1. Kernels
          2. 10.4.6.2. Results
      5. 10.5. Results and comments
      6. 10.6. Conclusion
      7. 10.7. Bibliography
    3. Chapter 11. A Dynamic Learning-based Approach to the Surveillance and Monitoring of Steam Generators in Prototype Fast Reactors
      1. 11.1. Introduction
      2. 11.2. Proposed method for the surveillance and monitoring of a steam generator
        1. 11.2.1. Learning and classification
        2. 11.2.2. Detecting the evolution of a class
        3. 11.2.3. Adapting a class after validating its evolution and creating a new class
        4. 11.2.4. Validating classes
        5. 11.2.5. Defining the parameters of the SS-DFKNN method
      3. 11.3. Results
        1. 11.3.1. Data analysis
        2. 11.3.2. Classification results
        3. 11.3.3. Designing an automaton to improve classification rates
      4. 11.4. Conclusion and perspectives
      5. 11.5. Bibliography
    4. Chapter 12. SVM Time-Frequency Classification for the Detection of Injection States
      1. 12.1. Introduction
      2. 12.2. Preliminary examination of the data
        1. 12.2.1. Approach
        2. 12.2.2. Spectral analysis of the data
          1. 12.2.2.1. Data format
          2. 12.2.2.2. Welch method
          3. 12.2.2.3. Spectral study
        3. 12.2.3. Class visualization
      3. 12.3. Detection algorithm
        1. 12.3.1. SVM implementation
          1. 12.3.1.1. Principle
          2. 12.3.1.2. Choice of attributes
          3. 12.3.1.3. Choice of kernel
        2. 12.3.2. Algorithm calibration
      4. 12.4. Role of sensors
      5. 12.5. Experimental results
      6. 12.6. Bibliography
    5. Chapter 13. Time and Frequency Domain Approaches for the Characterization of Injection States
      1. 13.1. Introduction
        1. 13.1.1. Framework of the study
        2. 13.1.2. Processing recordings
        3. 13.1.3. Identifying the injection zones
        4. 13.1.4. Extraction of “non-injection” zones
          1. 13.1.4.1. Database
      2. 13.2. Analyzing the statistical properties of spectral power densities
        1. 13.2.1. Methodology
          1. 13.2.1.1. Calculating the power spectral densities
          2. 13.2.1.2. Modeling and recognition
        2. 13.2.2. Results
          1. 13.2.2.1. Distinction between “injection” and “non-injection”
          2. 13.2.2.2. Water–argon distinction
          3. 13.2.2.3. Validation and analysis of the results
          4. 13.2.2.4. Separation between “non-injection” and “with injection”
          5. 13.2.2.5. Water and argon distinction
        3. 13.2.3. Exploring implementation in a new installation
          1. 13.2.3.1. Issues
          2. 13.2.3.2. Detecting leaks
          3. 13.2.3.3. Conclusions on the PSD approach
      3. 13.3. Analysis of the filtering characteristics
        1. 13.3.1. Estimating filtering characteristics using an AR model
        2. 13.3.2. Comparing filtering characteristics
          1. 13.3.2.1. Detecting leaks using SMD analysis
          2. 13.3.2.2. “Non-injection” zone after the first injection
          3. 13.3.2.3. Injection zones
          4. 13.3.2.4. Non-injection zones after injection
        3. 13.3.3. A leak detection algorithm
        4. 13.3.4. Conclusions on the autoregressive signal modeling-based approach
      4. 13.4. Conclusion on frequential and temporal approaches
      5. 13.5. Bibliography
  9. Part 4. Human, Organizational and Environmental Factors in Risk Analysis
    1. Chapter 14. Risk Analysis and Management in Systems Integrating Technical, Human, Organizational and Environmental Aspects
      1. 14.1. Aims of the project
      2. 14.2. State of the art
        1. 14.2.1. Context of the study
        2. 14.2.2. Towards an “integrated” approach to risk: combining several specialist disciplines
      3. 14.3. Integrated risk analysis
        1. 14.3.1. Concepts
        2. 14.3.2. A description of the approach
          1. 14.3.2.1. Technical dimension
          2. 14.3.2.2. Human dimension
          3. 14.3.2.3. Organization dimension
      4. 14.4. Accounting for uncertainty in risk analysis
        1. 14.4.1. Different kinds and sources of uncertainty
        2. 14.4.2. Frameworks for modeling uncertainty
          1. 14.4.2.1. Probability theory
          2. 14.4.2.2. Interval theory
          3. 14.4.2.3. Possibility theory
          4. 14.4.2.4. Evidence theory
          5. 14.4.2.5. Dezert-Smarandache theory
          6. 14.4.2.6. Imprecise probability theory
      5. 14.5. Modeling risk for a quantitative assessment of risk
        1. 14.5.1. Bayesian networks
        2. 14.5.2. Evaluating risk beyond a probabilistic framework
      6. 14.6. Conclusions and future perspectives
      7. 14.7. Bibliography
    2. Chapter 15. Integrating Human and Organizational Factors into the BCD Risk Analysis Model: An Influence Diagram-based approach
      1. 15.1. Introduction
      2. 15.2. Introduction of the BCD (benefit-cost-deficit) approach
      3. 15.3. Analysis model for human actions
        1. 15.3.1. Accounting for organizational and human factors
        2. 15.3.2. Influence diagrams
        3. 15.3.3. Structure and parameters associated with the risk analysis model
      4. 15.4. Example application
        1. 15.4.1. Description of the case study: industrial printing presses
        2. 15.4.2. Presentation of the model for the test case
      5. 15.5. Conclusion
      6. 15.6. Acknowledgements
      7. 15.7. Bibliography
  10. Conclusion
  11. List of Authors
  12. Index