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Diagnosability Analysis and FDI System Design for Uncertain Systems.

Bibliographic Details
Main Author: Eriksson, Daniel.
Format: eBook
Language:English
Published: Linköping : Linkopings Universitet, 2013.
Edition:1st ed.
Series:Linköping Studies in Science and Technology. Thesis Series
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • 1 Introduction
  • 1.1 Fault diagnosis
  • 1.1.1 Model based diagnosis
  • 1.2 Fault diagnosability analysis
  • 1.2.1 Utilizing diagnosability analysis for design of diagnosis systems
  • 1.2.2 The Kullback-Leibler divergence
  • 1.2.3 Engine misfire detection
  • 1.3 Scope
  • 1.4 Contributions
  • 1.5 Publications
  • References
  • Publications
  • A A method for quantitative fault diagnosability analysis of stochastic linear descriptor models
  • 1 Introduction
  • 2 Problem formulation
  • 3 Distinguishability
  • 3.1 Reformulating the model
  • 3.2 Stochastic characterization of fault modes
  • 3.3 Quantitative detectability and isolability
  • 4 Computation of distinguishability
  • 5 Relation to residual generators
  • 6 Diesel engine model analysis
  • 6.1 Model description
  • 6.2 Diagnosability analysis of the model
  • 7 Conclusions
  • References
  • B Using quantitative diagnosability analysis for optimal sensor placement
  • 1 Introduction
  • 2 Introductory example
  • 2.1 Sensor placement using deterministic method
  • 2.2 Analysis of minimal sensor sets using distinguishability
  • 3 Problem formulation
  • 4 Background theory
  • 4.1 Model
  • 4.2 Quantified diagnosability performance
  • 5 The small example revisited
  • 6 A greedy search approach
  • 7 Sensor placement using greedy search
  • 7.1 Model
  • 7.2 Analysis of the underdetermined model
  • 7.3 Analysis of the exactly determined model
  • 8 Conclusion
  • References
  • C A sequential test selection algorithm for fault isolation
  • 1 Introduction
  • 2 Problem formulation
  • 3 Background theory
  • 3.1 Distinguishability
  • 3.2 Relation of residual generators
  • 4 Generalization of distinguishability
  • 5 Sequential test selection
  • 5.1 Principles
  • 5.2 Algorithm
  • 6 Case study: DC circuit
  • 6.1 System
  • 6.2 Diagnosis algorithm
  • 6.3 Evaluation
  • 7 Tuning the test selection algorithm.
  • 7.1 Off-line
  • 7.2 On-line
  • 7.3 Other measures of diagnosability performance
  • 8 Conclusion
  • 9 Acknowledgment
  • References
  • D Flywheel angular velocity model for misfire simulation
  • 1 Introduction
  • 2 Model requirements
  • 3 Model
  • 3.1 Model outline
  • 3.2 Engine
  • 3.3 Driveline
  • 3.4 Modeling disturbances
  • 4 Model validation
  • 4.1 Experimental data
  • 4.2 Validation
  • 5 Conclusions
  • References
  • E Analysis and optimization with the Kullback-Leibler divergence for misfire detection using estimated torque
  • 1 Introduction
  • 2 Vehicle control system signals
  • 3 Analysis of the flywheel angular velocity signal
  • 4 The Kullback-Leibler divergence
  • 5 Torque estimation based on the angular velocity signal
  • 5.1 Analyzing misfire detectability performance of estimated torque signal
  • 6 An algorithm for misfire detection
  • 6.1 Algorithm outline
  • 6.2 Design of test quantity
  • 6.3 Thresholding
  • 7 Evaluation of the misfire detection algorithm
  • 8 Conclusions
  • 9 Future works
  • 10 Acknowledgment
  • References.

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