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Part 1. Industrial Issues
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Part 1. Industrial Issues
by Jean Arlat, Yves Vandenboomgaerde, Nada Matta
Supervision and Safety of Complex Systems
Cover
Title Page
Copyright
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. Supervison 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
4.3.1.1. The choice of use of Bayesian networks
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
4.4.5.1. First ambiguous situation: the case of parallel roads
4.4.5.2. Second ambiguous situation: the case of a road junction
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
5.4.2.1. Estimation of parameters of Wiener process
5.4.2.2. RUL estimation by simulation of a Wiener process
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
6.2.2.1. Observer design for fault detection
6.2.2.2. Robustness with respect to unknown inputs and sensitivity to faults
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
6.3.3.1. General method
6.3.3.2. Criteria for the choice of quasi-LPV form
6.3.4. Conclusion
6.4. Detection of switching and operating mode recognition without the explicit use of model parameters
6.4.1. Introduction
6.4.2. Diagnosis of SSs with linear modes
6.4.2.1. Formulation of the problem
6.4.2.2. Residual calculation for the estimation of switching time
6.4.2.3. Sensitivity of the residual to changes in mode
6.4.2.4. Residual based on data for recognizing the current mode
6.4.2.5. Tuning the method
6.4.2.6. Summary of the method
6.4.3. Diagnosis of a switching system with uncertain nonlinear modes
6.4.3.1. Model of a switching system with nonlinear modes
6.4.3.2. Residual generation
6.4.4. Conclusions
6.5. Modeling, observation and monitoring of switching systems: application to a 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
6.5.3.1. Overview of the converter faults
6.5.3.2. Diagnosis based on an observer
6.5.3.3. Simulation results
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. Acknowledgements
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?
8.3.2.1. Definition
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
8.5.3.1. Process variables and parameters
8.5.3.2. Component state variables
8.5.3.3. Information variables
8.5.3.4. MOCA-RP
8.5.4. Qualitative and quantitative results
8.5.4.1. Initial tests
8.5.4.2. Real-life tests
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 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
12.2.2.1. Data format
12.2.2.2. Welch method
12.2.2.3. Spectral study
12.2.3. Class visualization
12.3. Detection algorithm
12.3.1. SVM implementation
12.3.1.1. Principle
12.3.1.2. Choice of attributes
12.3.1.3. Choice of kernel
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
13.1.4.1. Database
13.2. Analyzing the statistical properties of spectral power densities
13.2.1. Methodology
13.2.1.1. Calculating the power spectral densities
13.2.1.2. Modeling and recognition
13.2.2. Results
13.2.2.1. Distinction between “injection” and “non-injection”
13.2.2.2. Water–argon distinction
13.2.2.3. Validation and analysis of the results
13.2.2.4. Separation between “non-injection” and “with injection”
13.2.2.5. Water and argon distinction
13.2.3. Exploring implementation in a new installation
13.2.3.1. Issues
13.2.3.2. Detecting leaks
13.2.3.3. Conclusions on the PSD approach
13.3. Analysis of the filtering characteristics
13.3.1. Estimating filtering characteristics using an AR model
13.3.2. Comparing filtering characteristics
13.3.2.1. Detecting leaks using SMD analysis
13.3.2.2. “Non-injection” zone after the first injection
13.3.2.3. Injection zones
13.3.2.4. Non-injection zones after injection
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
14.3.2.1. Technical dimension
14.3.2.2. Human dimension
14.3.2.3. Organization dimension
14.4. Accounting for uncertainty in risk analysis
14.4.1. Different kinds and sources of uncertainty
14.4.2. Frameworks for modeling uncertainty
14.4.2.1. Probability theory
14.4.2.2. Interval theory
14.4.2.3. Possibility theory
14.4.2.4. Evidence theory
14.4.2.5. Dezert-Smarandache theory
14.4.2.6. Imprecise probability theory
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. Acknowledgements
15.7. Bibliography
Conclusion
List of Authors
Index
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