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|a MiAaPQ
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|a Eriksson, Daniel.
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|a Diagnosability Analysis and FDI System Design for Uncertain Systems.
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|a 1st ed.
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|a Linköping :
|b Linkopings Universitet,
|c 2013.
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|c {copy}2013.
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|a 1 online resource (177 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Linköping Studies in Science and Technology. Thesis Series ;
|v v.1584
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|a 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.
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|a 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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|a Description based on publisher supplied metadata and other sources.
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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|a Electronic books.
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|i Print version:
|a Eriksson, Daniel
|t Diagnosability Analysis and FDI System Design for Uncertain Systems
|d Linköping : Linkopings Universitet,c2013
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| 797 |
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|a ProQuest (Firm)
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| 830 |
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|a Linköping Studies in Science and Technology. Thesis Series
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| 856 |
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|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=30400800
|z Click to View
|
