Diagnosability Analysis and FDI System Design for Uncertain Systems.

• Superior document: Linköping Studies in Science and Technology. Thesis Series ; v.1584
• Author: Eriksson, Daniel.
• Place / Publishing House: Linköping : : Linkopings Universitet,, 2013.; {copy}2013.
• Year of Publication: 2013
• Edition: 1st ed.
• Language: English
• Series: Linköping Studies in Science and Technology. Thesis Series
• Physical Description: 1 online resource (177 pages)

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.