Causality Assignment and Model Approximation for Hybrid Bond Graph: Fault Diagnosis Perspectives
Bond graph (BG) is an effective tool for modeling complex systems and it has been proven useful for fault detection and isolation (FDI) for continuous systems. BG provides the causal relations between system's variables which allow FDI algorithms to be developed systematically from the graph. I...
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| Veröffentlicht in: | IEEE transactions on automation science and engineering 2010-07, Vol.7 (3), p.570-580 |
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| creator | Chang Boon Low Danwei Wang Arogeti, Shai Jing Bing Zhang |
| description | Bond graph (BG) is an effective tool for modeling complex systems and it has been proven useful for fault detection and isolation (FDI) for continuous systems. BG provides the causal relations between system's variables which allow FDI algorithms to be developed systematically from the graph. In the same spirit, Hybrid bond graph (HBG) is a BG-based modeling approach which provides an avenue to model complex hybrid systems. However, due to mode-varying causality properties of HBG, HBG has not been efficiently-exploited for fault diagnosis. In this work, a comprehensive study on the HBG from FDI viewpoints is presented. Some properties pertaining to the HBG are gained in the study. Based on these findings, a causality assignment procedure and a model approximation technique are developed to achieve a HBG with a desirable causality assignment that leads a unified description of system's behavior. These results lay a foundation for quantitative FDI design for complex hybrid systems. |
| doi_str_mv | 10.1109/TASE.2009.2026731 |
| format | Article |
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BG provides the causal relations between system's variables which allow FDI algorithms to be developed systematically from the graph. In the same spirit, Hybrid bond graph (HBG) is a BG-based modeling approach which provides an avenue to model complex hybrid systems. However, due to mode-varying causality properties of HBG, HBG has not been efficiently-exploited for fault diagnosis. In this work, a comprehensive study on the HBG from FDI viewpoints is presented. Some properties pertaining to the HBG are gained in the study. Based on these findings, a causality assignment procedure and a model approximation technique are developed to achieve a HBG with a desirable causality assignment that leads a unified description of system's behavior. 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(IEEE) Jul 2010</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c354t-9dbfbb7ddb6b32f179a115b1c524395661299124ea2a9c2042ee7736fa4ffc2d3</citedby><cites>FETCH-LOGICAL-c354t-9dbfbb7ddb6b32f179a115b1c524395661299124ea2a9c2042ee7736fa4ffc2d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5262981$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5262981$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23020518$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Chang Boon Low</creatorcontrib><creatorcontrib>Danwei Wang</creatorcontrib><creatorcontrib>Arogeti, Shai</creatorcontrib><creatorcontrib>Jing Bing Zhang</creatorcontrib><title>Causality Assignment and Model Approximation for Hybrid Bond Graph: Fault Diagnosis Perspectives</title><title>IEEE transactions on automation science and engineering</title><addtitle>TASE</addtitle><description>Bond graph (BG) is an effective tool for modeling complex systems and it has been proven useful for fault detection and isolation (FDI) for continuous systems. BG provides the causal relations between system's variables which allow FDI algorithms to be developed systematically from the graph. In the same spirit, Hybrid bond graph (HBG) is a BG-based modeling approach which provides an avenue to model complex hybrid systems. However, due to mode-varying causality properties of HBG, HBG has not been efficiently-exploited for fault diagnosis. In this work, a comprehensive study on the HBG from FDI viewpoints is presented. Some properties pertaining to the HBG are gained in the study. Based on these findings, a causality assignment procedure and a model approximation technique are developed to achieve a HBG with a desirable causality assignment that leads a unified description of system's behavior. These results lay a foundation for quantitative FDI design for complex hybrid systems.</description><subject>Algorithms</subject><subject>Analytical models</subject><subject>Applied sciences</subject><subject>Approximation</subject><subject>Automation</subject><subject>Bond graphs</subject><subject>Bonding</subject><subject>Causality assignment</subject><subject>Complex systems</subject><subject>Computer science; control theory; systems</subject><subject>Continuous time systems</subject><subject>Control theory. Systems</subject><subject>Equations</subject><subject>Exact sciences and technology</subject><subject>Fasteners</subject><subject>Fault detection</subject><subject>Fault diagnosis</subject><subject>fault diagnosis perspective</subject><subject>hybrid bond graph (HBG)</subject><subject>Hybrid systems</subject><subject>Industrial metrology. Testing</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Mechanical engineering. Machine design</subject><subject>Modelling and identification</subject><subject>Monitoring</subject><subject>Real time systems</subject><subject>Safety</subject><issn>1545-5955</issn><issn>1558-3783</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1v1DAQhiNEJUrLD0BcLCTEKcVjx47NbVn6gdSqSJSzcRy7uMrGqSdB3X9fr3bVA5eZkeaZ0aunqt4DPQOg-svd6tf5GaNUl8Jky-FVdQxCqJq3ir_ezY2ohRbiTfUW8YFS1ihNj6s_a7ugHeK8JSvEeD9u_DgTO_bkJvV-IKtpyukpbuwc00hCyuRq2-XYk2-pMJfZTn-_kgu7DDP5Hu39mDAi-ekzTt7N8Z_H0-oo2AH9u0M_qX5fnN-tr-rr28sf69V17bho5lr3Xei6tu872XEWoNUWQHTgBGu4FlIC0xpY4y2z2jHaMO_blstgmxAc6_lJ9Xn_t-R9XDzOZhPR-WGwo08LGgVKcZBUF_Ljf-RDWvJYwhlJlQQpOC8Q7CGXE2L2wUy5WMhbA9TsjJudcbMzbg7Gy82nw2OLzg4h29FFfDlknDIqQBXuw56L3vuXtWCSaQX8GVaBiXQ</recordid><startdate>20100701</startdate><enddate>20100701</enddate><creator>Chang Boon Low</creator><creator>Danwei Wang</creator><creator>Arogeti, Shai</creator><creator>Jing Bing Zhang</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope></search><sort><creationdate>20100701</creationdate><title>Causality Assignment and Model Approximation for Hybrid Bond Graph: Fault Diagnosis Perspectives</title><author>Chang Boon Low ; Danwei Wang ; Arogeti, Shai ; Jing Bing Zhang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c354t-9dbfbb7ddb6b32f179a115b1c524395661299124ea2a9c2042ee7736fa4ffc2d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithms</topic><topic>Analytical models</topic><topic>Applied sciences</topic><topic>Approximation</topic><topic>Automation</topic><topic>Bond graphs</topic><topic>Bonding</topic><topic>Causality assignment</topic><topic>Complex systems</topic><topic>Computer science; control theory; systems</topic><topic>Continuous time systems</topic><topic>Control theory. Systems</topic><topic>Equations</topic><topic>Exact sciences and technology</topic><topic>Fasteners</topic><topic>Fault detection</topic><topic>Fault diagnosis</topic><topic>fault diagnosis perspective</topic><topic>hybrid bond graph (HBG)</topic><topic>Hybrid systems</topic><topic>Industrial metrology. Testing</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Mechanical engineering. Machine design</topic><topic>Modelling and identification</topic><topic>Monitoring</topic><topic>Real time systems</topic><topic>Safety</topic><toplevel>online_resources</toplevel><creatorcontrib>Chang Boon Low</creatorcontrib><creatorcontrib>Danwei Wang</creatorcontrib><creatorcontrib>Arogeti, Shai</creatorcontrib><creatorcontrib>Jing Bing Zhang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on automation science and engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chang Boon Low</au><au>Danwei Wang</au><au>Arogeti, Shai</au><au>Jing Bing Zhang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Causality Assignment and Model Approximation for Hybrid Bond Graph: Fault Diagnosis Perspectives</atitle><jtitle>IEEE transactions on automation science and engineering</jtitle><stitle>TASE</stitle><date>2010-07-01</date><risdate>2010</risdate><volume>7</volume><issue>3</issue><spage>570</spage><epage>580</epage><pages>570-580</pages><issn>1545-5955</issn><eissn>1558-3783</eissn><coden>ITASC7</coden><abstract>Bond graph (BG) is an effective tool for modeling complex systems and it has been proven useful for fault detection and isolation (FDI) for continuous systems. BG provides the causal relations between system's variables which allow FDI algorithms to be developed systematically from the graph. In the same spirit, Hybrid bond graph (HBG) is a BG-based modeling approach which provides an avenue to model complex hybrid systems. However, due to mode-varying causality properties of HBG, HBG has not been efficiently-exploited for fault diagnosis. In this work, a comprehensive study on the HBG from FDI viewpoints is presented. Some properties pertaining to the HBG are gained in the study. Based on these findings, a causality assignment procedure and a model approximation technique are developed to achieve a HBG with a desirable causality assignment that leads a unified description of system's behavior. These results lay a foundation for quantitative FDI design for complex hybrid systems.</abstract><cop>Piscataway, NJ</cop><pub>IEEE</pub><doi>10.1109/TASE.2009.2026731</doi><tpages>11</tpages></addata></record> |
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| subjects | Algorithms Analytical models Applied sciences Approximation Automation Bond graphs Bonding Causality assignment Complex systems Computer science control theory systems Continuous time systems Control theory. Systems Equations Exact sciences and technology Fasteners Fault detection Fault diagnosis fault diagnosis perspective hybrid bond graph (HBG) Hybrid systems Industrial metrology. Testing Mathematical analysis Mathematical models Mechanical engineering. Machine design Modelling and identification Monitoring Real time systems Safety |
| title | Causality Assignment and Model Approximation for Hybrid Bond Graph: Fault Diagnosis Perspectives |
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