Diagnosability Analysis and FDI System Design for Uncertain Systems.
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Superior document: | Linköping Studies in Science and Technology. Thesis Series ; v.1584 |
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Place / Publishing House: | Linköping : : Linkopings Universitet,, 2013. {copy}2013. |
出版年: | 2013 |
版: | 1st ed. |
语言: | English |
丛编: | Linköping Studies in Science and Technology. Thesis Series
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实物描述: | 1 online resource (177 pages) |
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Eriksson, Daniel. Diagnosability Analysis and FDI System Design for Uncertain Systems. 1st ed. Linköping : Linkopings Universitet, 2013. {copy}2013. 1 online resource (177 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Linköping Studies in Science and Technology. Thesis Series ; v.1584 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Print version: Eriksson, Daniel Diagnosability Analysis and FDI System Design for Uncertain Systems Linköping : Linkopings Universitet,c2013 ProQuest (Firm) Linköping Studies in Science and Technology. Thesis Series https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30400800 Click to View |
language |
English |
format |
eBook |
author |
Eriksson, Daniel. |
spellingShingle |
Eriksson, Daniel. Diagnosability Analysis and FDI System Design for Uncertain Systems. Linköping Studies in Science and Technology. Thesis Series ; 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. |
author_facet |
Eriksson, Daniel. |
author_variant |
d e de |
author_sort |
Eriksson, Daniel. |
title |
Diagnosability Analysis and FDI System Design for Uncertain Systems. |
title_full |
Diagnosability Analysis and FDI System Design for Uncertain Systems. |
title_fullStr |
Diagnosability Analysis and FDI System Design for Uncertain Systems. |
title_full_unstemmed |
Diagnosability Analysis and FDI System Design for Uncertain Systems. |
title_auth |
Diagnosability Analysis and FDI System Design for Uncertain Systems. |
title_new |
Diagnosability Analysis and FDI System Design for Uncertain Systems. |
title_sort |
diagnosability analysis and fdi system design for uncertain systems. |
series |
Linköping Studies in Science and Technology. Thesis Series ; |
series2 |
Linköping Studies in Science and Technology. Thesis Series ; |
publisher |
Linkopings Universitet, |
publishDate |
2013 |
physical |
1 online resource (177 pages) |
edition |
1st ed. |
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. |
isbn |
9789175196527 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30400800 |
illustrated |
Not Illustrated |
oclc_num |
1372398699 |
work_keys_str_mv |
AT erikssondaniel diagnosabilityanalysisandfdisystemdesignforuncertainsystems |
status_str |
n |
ids_txt_mv |
(MiAaPQ)50030400800 (Au-PeEL)EBL30400800 (OCoLC)1372398699 |
carrierType_str_mv |
cr |
hierarchy_parent_title |
Linköping Studies in Science and Technology. Thesis Series ; v.1584 |
is_hierarchy_title |
Diagnosability Analysis and FDI System Design for Uncertain Systems. |
container_title |
Linköping Studies in Science and Technology. Thesis Series ; v.1584 |
marc_error |
Info : MARC8 translation shorter than ISO-8859-1, choosing MARC8. --- [ 856 : z ] |
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