An Intelligent Diagnostic/Prognostic Framework for Automotive Electrical Systems
Automotive systems are becoming increasingly dependent on electrical components, computer control, and sensors. It has become extremely critical to detect faults in the electrical system and predict the remaining useful life of failing components. This paper introduces an integrated methodology for...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 357 |
|---|---|
| container_issue | |
| container_start_page | 352 |
| container_title | |
| container_volume | |
| creator | Abbas, Manzar Ferri, Aldo A. Orchard, Marcos E. Vachtsevanos, George J. |
| description | Automotive systems are becoming increasingly dependent on electrical components, computer control, and sensors. It has become extremely critical to detect faults in the electrical system and predict the remaining useful life of failing components. This paper introduces an integrated methodology for monitoring, modeling, data processing, fault diagnosis, and failure prognosis of critical electrical components such as the battery. The enabling technologies include signal processing, sensor selection and placement, selection and extraction of optimum condition indicators, and accurate fault diagnosis and failure prognosis algorithms that are based on both the physics of failure models and Bayesian estimation methods. The proposed architecture is implementable on-board an Electronic Control Unit (ECU) requiring minimum computational resources. Potential benefits include reduction in maintenance costs, improved asset reliability and availability and longer life of critical components. |
| doi_str_mv | 10.1109/IVS.2007.4290139 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4290139</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4290139</ieee_id><sourcerecordid>4290139</sourcerecordid><originalsourceid>FETCH-LOGICAL-c137t-48d33b839bcb271fa26c3194f0a4736b6adde22896716f1c4c119886838f9c63</originalsourceid><addsrcrecordid>eNpFkMtKAzEYRuMNbKt7wU1eYKb5k0wuy6G2OlCw0OK2ZDKZEp2LJFHp27twwNV34MBZfAg9AMkBiF5Wb_ucEiJzTjUBpi_QHDjlHIhQcIlmVHCaSQr86l9Ido1moBlkpFDyFs1jfCekKCiFGdqVA66G5LrOn9yQ8JM3p2GMydvlLowT4k0wvfsZwwdux4DLrzT2Y_LfDq87Z1Pw1nR4f47J9fEO3bSmi-5-2gU6bNaH1Uu2fX2uVuU2s8BkyrhqGKsV07WtqYTWUGEZaN4SwyUTtTBN4yhVWkgQLVhuAbRSQjHVaivYAj3-Zb1z7vgZfG_C-Ti9wn4B68JSCA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>An Intelligent Diagnostic/Prognostic Framework for Automotive Electrical Systems</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Abbas, Manzar ; Ferri, Aldo A. ; Orchard, Marcos E. ; Vachtsevanos, George J.</creator><creatorcontrib>Abbas, Manzar ; Ferri, Aldo A. ; Orchard, Marcos E. ; Vachtsevanos, George J.</creatorcontrib><description>Automotive systems are becoming increasingly dependent on electrical components, computer control, and sensors. It has become extremely critical to detect faults in the electrical system and predict the remaining useful life of failing components. This paper introduces an integrated methodology for monitoring, modeling, data processing, fault diagnosis, and failure prognosis of critical electrical components such as the battery. The enabling technologies include signal processing, sensor selection and placement, selection and extraction of optimum condition indicators, and accurate fault diagnosis and failure prognosis algorithms that are based on both the physics of failure models and Bayesian estimation methods. The proposed architecture is implementable on-board an Electronic Control Unit (ECU) requiring minimum computational resources. Potential benefits include reduction in maintenance costs, improved asset reliability and availability and longer life of critical components.</description><identifier>ISSN: 1931-0587</identifier><identifier>ISBN: 1424410673</identifier><identifier>ISBN: 9781424410675</identifier><identifier>EISSN: 2642-7214</identifier><identifier>EISBN: 1424410681</identifier><identifier>EISBN: 9781424410682</identifier><identifier>DOI: 10.1109/IVS.2007.4290139</identifier><language>eng</language><publisher>IEEE</publisher><subject>Automotive engineering ; Computerized monitoring ; Condition monitoring ; Control systems ; Electrical fault detection ; Fault diagnosis ; Intelligent sensors ; Intelligent vehicles ; Sensor systems ; Signal processing algorithms</subject><ispartof>2007 IEEE Intelligent Vehicles Symposium, 2007, p.352-357</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c137t-48d33b839bcb271fa26c3194f0a4736b6adde22896716f1c4c119886838f9c63</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4290139$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4290139$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Abbas, Manzar</creatorcontrib><creatorcontrib>Ferri, Aldo A.</creatorcontrib><creatorcontrib>Orchard, Marcos E.</creatorcontrib><creatorcontrib>Vachtsevanos, George J.</creatorcontrib><title>An Intelligent Diagnostic/Prognostic Framework for Automotive Electrical Systems</title><title>2007 IEEE Intelligent Vehicles Symposium</title><addtitle>IVS</addtitle><description>Automotive systems are becoming increasingly dependent on electrical components, computer control, and sensors. It has become extremely critical to detect faults in the electrical system and predict the remaining useful life of failing components. This paper introduces an integrated methodology for monitoring, modeling, data processing, fault diagnosis, and failure prognosis of critical electrical components such as the battery. The enabling technologies include signal processing, sensor selection and placement, selection and extraction of optimum condition indicators, and accurate fault diagnosis and failure prognosis algorithms that are based on both the physics of failure models and Bayesian estimation methods. The proposed architecture is implementable on-board an Electronic Control Unit (ECU) requiring minimum computational resources. Potential benefits include reduction in maintenance costs, improved asset reliability and availability and longer life of critical components.</description><subject>Automotive engineering</subject><subject>Computerized monitoring</subject><subject>Condition monitoring</subject><subject>Control systems</subject><subject>Electrical fault detection</subject><subject>Fault diagnosis</subject><subject>Intelligent sensors</subject><subject>Intelligent vehicles</subject><subject>Sensor systems</subject><subject>Signal processing algorithms</subject><issn>1931-0587</issn><issn>2642-7214</issn><isbn>1424410673</isbn><isbn>9781424410675</isbn><isbn>1424410681</isbn><isbn>9781424410682</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkMtKAzEYRuMNbKt7wU1eYKb5k0wuy6G2OlCw0OK2ZDKZEp2LJFHp27twwNV34MBZfAg9AMkBiF5Wb_ucEiJzTjUBpi_QHDjlHIhQcIlmVHCaSQr86l9Ido1moBlkpFDyFs1jfCekKCiFGdqVA66G5LrOn9yQ8JM3p2GMydvlLowT4k0wvfsZwwdux4DLrzT2Y_LfDq87Z1Pw1nR4f47J9fEO3bSmi-5-2gU6bNaH1Uu2fX2uVuU2s8BkyrhqGKsV07WtqYTWUGEZaN4SwyUTtTBN4yhVWkgQLVhuAbRSQjHVaivYAj3-Zb1z7vgZfG_C-Ti9wn4B68JSCA</recordid><startdate>200706</startdate><enddate>200706</enddate><creator>Abbas, Manzar</creator><creator>Ferri, Aldo A.</creator><creator>Orchard, Marcos E.</creator><creator>Vachtsevanos, George J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200706</creationdate><title>An Intelligent Diagnostic/Prognostic Framework for Automotive Electrical Systems</title><author>Abbas, Manzar ; Ferri, Aldo A. ; Orchard, Marcos E. ; Vachtsevanos, George J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c137t-48d33b839bcb271fa26c3194f0a4736b6adde22896716f1c4c119886838f9c63</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Automotive engineering</topic><topic>Computerized monitoring</topic><topic>Condition monitoring</topic><topic>Control systems</topic><topic>Electrical fault detection</topic><topic>Fault diagnosis</topic><topic>Intelligent sensors</topic><topic>Intelligent vehicles</topic><topic>Sensor systems</topic><topic>Signal processing algorithms</topic><toplevel>online_resources</toplevel><creatorcontrib>Abbas, Manzar</creatorcontrib><creatorcontrib>Ferri, Aldo A.</creatorcontrib><creatorcontrib>Orchard, Marcos E.</creatorcontrib><creatorcontrib>Vachtsevanos, George J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Abbas, Manzar</au><au>Ferri, Aldo A.</au><au>Orchard, Marcos E.</au><au>Vachtsevanos, George J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An Intelligent Diagnostic/Prognostic Framework for Automotive Electrical Systems</atitle><btitle>2007 IEEE Intelligent Vehicles Symposium</btitle><stitle>IVS</stitle><date>2007-06</date><risdate>2007</risdate><spage>352</spage><epage>357</epage><pages>352-357</pages><issn>1931-0587</issn><eissn>2642-7214</eissn><isbn>1424410673</isbn><isbn>9781424410675</isbn><eisbn>1424410681</eisbn><eisbn>9781424410682</eisbn><abstract>Automotive systems are becoming increasingly dependent on electrical components, computer control, and sensors. It has become extremely critical to detect faults in the electrical system and predict the remaining useful life of failing components. This paper introduces an integrated methodology for monitoring, modeling, data processing, fault diagnosis, and failure prognosis of critical electrical components such as the battery. The enabling technologies include signal processing, sensor selection and placement, selection and extraction of optimum condition indicators, and accurate fault diagnosis and failure prognosis algorithms that are based on both the physics of failure models and Bayesian estimation methods. The proposed architecture is implementable on-board an Electronic Control Unit (ECU) requiring minimum computational resources. Potential benefits include reduction in maintenance costs, improved asset reliability and availability and longer life of critical components.</abstract><pub>IEEE</pub><doi>10.1109/IVS.2007.4290139</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1931-0587 |
| ispartof | 2007 IEEE Intelligent Vehicles Symposium, 2007, p.352-357 |
| issn | 1931-0587 2642-7214 |
| language | eng |
| recordid | cdi_ieee_primary_4290139 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Automotive engineering Computerized monitoring Condition monitoring Control systems Electrical fault detection Fault diagnosis Intelligent sensors Intelligent vehicles Sensor systems Signal processing algorithms |
| title | An Intelligent Diagnostic/Prognostic Framework for Automotive Electrical Systems |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T00%3A50%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=An%20Intelligent%20Diagnostic/Prognostic%20Framework%20for%20Automotive%20Electrical%20Systems&rft.btitle=2007%20IEEE%20Intelligent%20Vehicles%20Symposium&rft.au=Abbas,%20Manzar&rft.date=2007-06&rft.spage=352&rft.epage=357&rft.pages=352-357&rft.issn=1931-0587&rft.eissn=2642-7214&rft.isbn=1424410673&rft.isbn_list=9781424410675&rft_id=info:doi/10.1109/IVS.2007.4290139&rft_dat=%3Cieee_6IE%3E4290139%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1424410681&rft.eisbn_list=9781424410682&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4290139&rfr_iscdi=true |