Automatic tire pressure fault monitor using wavelet-based probability density estimation
This paper is devoted to the problem of tire pressure monitoring and tire fault detection. Based on wavelet package transformation, the density of the tire's response vibration caused by the stochastic ground excitation is analyzed. Then using RBF neural networks to learn and classify the diffe...
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| creator | Li Li Fei-Yue Wang Qunzhi Zhou Guoling Shan |
| description | This paper is devoted to the problem of tire pressure monitoring and tire fault detection. Based on wavelet package transformation, the density of the tire's response vibration caused by the stochastic ground excitation is analyzed. Then using RBF neural networks to learn and classify the different types of vibration response, an automatic abnormal tire pressure detector is built. Theoretical analysis and simulation results show the I effectives of this new fault detector. |
| doi_str_mv | 10.1109/IVS.2003.1212887 |
| format | Conference Proceeding |
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Based on wavelet package transformation, the density of the tire's response vibration caused by the stochastic ground excitation is analyzed. Then using RBF neural networks to learn and classify the different types of vibration response, an automatic abnormal tire pressure detector is built. Theoretical analysis and simulation results show the I effectives of this new fault detector.</description><identifier>ISBN: 9780780378483</identifier><identifier>ISBN: 0780378482</identifier><identifier>DOI: 10.1109/IVS.2003.1212887</identifier><language>eng</language><publisher>IEEE</publisher><subject>Automation ; Computerized monitoring ; Fault detection ; Friction ; Intelligent systems ; Laboratories ; Neural networks ; Stochastic processes ; Tires ; Wavelet analysis</subject><ispartof>IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. 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No.03TH8683)</title><addtitle>IVS</addtitle><description>This paper is devoted to the problem of tire pressure monitoring and tire fault detection. Based on wavelet package transformation, the density of the tire's response vibration caused by the stochastic ground excitation is analyzed. Then using RBF neural networks to learn and classify the different types of vibration response, an automatic abnormal tire pressure detector is built. Theoretical analysis and simulation results show the I effectives of this new fault detector.</description><subject>Automation</subject><subject>Computerized monitoring</subject><subject>Fault detection</subject><subject>Friction</subject><subject>Intelligent systems</subject><subject>Laboratories</subject><subject>Neural networks</subject><subject>Stochastic processes</subject><subject>Tires</subject><subject>Wavelet analysis</subject><isbn>9780780378483</isbn><isbn>0780378482</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2003</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotT0trwzAYM4zBRpf7YBf_gWT-bCe2j6XsUSjssLXsVvz4MjzSpMTORv_9UlYhkA5CQoTcA6sAmHlc794rzpiogAPXWl2RwijNZgqlpRY3pEjpm82QtWykuiWfyykPB5ujpzmOSI8jpjTNprVTl-lh6GMeRjql2H_RX_uDHebS2YRhjg7OutjFfKIB-3RWTDme24b-jly3tktYXHRBts9PH6vXcvP2sl4tN2UEVeeyZcCN4RatN8Ib7RqvpFHeKRXQcg_M1S20ymNowIZGOgSOyMDzgC5YsSAP_70REffHcZ4fT_vLf_EH-7FTzg</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Li Li</creator><creator>Fei-Yue Wang</creator><creator>Qunzhi Zhou</creator><creator>Guoling Shan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2003</creationdate><title>Automatic tire pressure fault monitor using wavelet-based probability density estimation</title><author>Li Li ; Fei-Yue Wang ; Qunzhi Zhou ; Guoling Shan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-f012992aeac93c98b6c7497cb77dea2c10b5f1f7ced61ad64be12ee01c2debda3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Automation</topic><topic>Computerized monitoring</topic><topic>Fault detection</topic><topic>Friction</topic><topic>Intelligent systems</topic><topic>Laboratories</topic><topic>Neural networks</topic><topic>Stochastic processes</topic><topic>Tires</topic><topic>Wavelet analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Li Li</creatorcontrib><creatorcontrib>Fei-Yue Wang</creatorcontrib><creatorcontrib>Qunzhi Zhou</creatorcontrib><creatorcontrib>Guoling Shan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li Li</au><au>Fei-Yue Wang</au><au>Qunzhi Zhou</au><au>Guoling Shan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automatic tire pressure fault monitor using wavelet-based probability density estimation</atitle><btitle>IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683)</btitle><stitle>IVS</stitle><date>2003</date><risdate>2003</risdate><spage>80</spage><epage>84</epage><pages>80-84</pages><isbn>9780780378483</isbn><isbn>0780378482</isbn><abstract>This paper is devoted to the problem of tire pressure monitoring and tire fault detection. Based on wavelet package transformation, the density of the tire's response vibration caused by the stochastic ground excitation is analyzed. Then using RBF neural networks to learn and classify the different types of vibration response, an automatic abnormal tire pressure detector is built. Theoretical analysis and simulation results show the I effectives of this new fault detector.</abstract><pub>IEEE</pub><doi>10.1109/IVS.2003.1212887</doi><tpages>5</tpages></addata></record> |
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| ispartof | IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), 2003, p.80-84 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Automation Computerized monitoring Fault detection Friction Intelligent systems Laboratories Neural networks Stochastic processes Tires Wavelet analysis |
| title | Automatic tire pressure fault monitor using wavelet-based probability density estimation |
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