Fault Diagnosis Method of Exhaust System of Port Vehicle
For the problem that the current fault diagnosis efficiency of vehicle exhaust system based on cold test is low, this paper proposes a fault diagnosis method of port vehicle exhaust system using principal component analysis method. This method used the historical data aggregating normal working cond...
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| Veröffentlicht in: | Journal of coastal research 2019-05, Vol.83 (sp1), p.469-473 |
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| creator | Ji, Wujun Li, Bing |
| description | For the problem that the current fault diagnosis efficiency of vehicle exhaust system based on cold test is low, this paper proposes a fault diagnosis method of port vehicle exhaust system using principal component analysis method. This method used the historical data aggregating normal working conditions of exhaust system, and found the principal component model of causality among variables in the process of expressing the normal working condition according to the statistical mode. If the real-time monitoring data did not correspond with the principal component model, we could determine the existence of fault in the exhaust system, then used change of each variable of measured data to analyze the variance contribution rate of principal component model, realizing fault diagnosis. Experimental result shows that the fault diagnosis noise of the proposed method is about 50dB lower than that of other methods, and the diagnostic stability and diagnosis rate are improved. Ji, W. and Li, B., 2018. Fault diagnosis method of exhaust system of port vehicle. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 469–473. Coconut Creek (Florida), ISSN 0749-0208. |
| doi_str_mv | 10.2112/SI83-078.1 |
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This method used the historical data aggregating normal working conditions of exhaust system, and found the principal component model of causality among variables in the process of expressing the normal working condition according to the statistical mode. If the real-time monitoring data did not correspond with the principal component model, we could determine the existence of fault in the exhaust system, then used change of each variable of measured data to analyze the variance contribution rate of principal component model, realizing fault diagnosis. Experimental result shows that the fault diagnosis noise of the proposed method is about 50dB lower than that of other methods, and the diagnostic stability and diagnosis rate are improved. Ji, W. and Li, B., 2018. Fault diagnosis method of exhaust system of port vehicle. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 469–473. 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This method used the historical data aggregating normal working conditions of exhaust system, and found the principal component model of causality among variables in the process of expressing the normal working condition according to the statistical mode. If the real-time monitoring data did not correspond with the principal component model, we could determine the existence of fault in the exhaust system, then used change of each variable of measured data to analyze the variance contribution rate of principal component model, realizing fault diagnosis. Experimental result shows that the fault diagnosis noise of the proposed method is about 50dB lower than that of other methods, and the diagnostic stability and diagnosis rate are improved. Ji, W. and Li, B., 2018. Fault diagnosis method of exhaust system of port vehicle. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 469–473. Coconut Creek (Florida), ISSN 0749-0208.</description><subject>Algorithms</subject><subject>Automation</subject><subject>Batch processes</subject><subject>Coasts</subject><subject>Computer simulation</subject><subject>Data</subject><subject>Data processing</subject><subject>Decomposition</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Electricity distribution</subject><subject>exhaust system</subject><subject>Exhaust systems</subject><subject>Fault diagnosis</subject><subject>History</subject><subject>Methods</subject><subject>Ocean engineering</subject><subject>PORT ENGINEERING</subject><subject>Port vehicle</subject><subject>Ports</subject><subject>Principal components analysis</subject><subject>Science</subject><subject>Signal processing</subject><subject>Stability</subject><subject>Variables</subject><subject>Variance analysis</subject><subject>Vehicle emissions</subject><subject>Vehicles</subject><subject>Working conditions</subject><issn>0749-0208</issn><issn>1551-5036</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMFLwzAYxYMoOKcX70LBiwid35cmaXqUuelgojD1GpIudR3bMpMU3H9vS8Wjpw_e-_HexyPkEmFEEendYiazFHI5wiMyQM4x5ZCJYzKAnBUpUJCn5CyENQAKyfIBkVPdbGLyUOvPnQt1SJ5tXLll4qpk8r3STYjJ4hCi3XbKq_Mx-bCrutzYc3JS6U2wF793SN6nk7fxUzp_eZyN7-epyZDFdIkl1dIwgxIMFCIHVlBRcF4ga3_gtGSSSQSQVatVvCjAGCgzYbTVUi6zIbnuc_fefTU2RLV2jd-1lYpihlwKmUNL3fZU6V0I3lZq7-ut9geFoLplVLeMapdR2MJXPbwO0fk_kgrOMgDa-je9b2rndva_qB8Gs2jU</recordid><startdate>20190504</startdate><enddate>20190504</enddate><creator>Ji, Wujun</creator><creator>Li, Bing</creator><general>Coastal Education and Research Foundation</general><general>COASTAL EDUCATION & RESEARCH FOUNDATION, INC. 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Diagnosis Method of Exhaust System of Port Vehicle</title><author>Ji, Wujun ; Li, Bing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b314t-d1c2a8b4b180b09670492695591401652c48481008f559f5990bb0c36baea88d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Automation</topic><topic>Batch processes</topic><topic>Coasts</topic><topic>Computer simulation</topic><topic>Data</topic><topic>Data processing</topic><topic>Decomposition</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Electricity distribution</topic><topic>exhaust system</topic><topic>Exhaust systems</topic><topic>Fault diagnosis</topic><topic>History</topic><topic>Methods</topic><topic>Ocean engineering</topic><topic>PORT ENGINEERING</topic><topic>Port vehicle</topic><topic>Ports</topic><topic>Principal components analysis</topic><topic>Science</topic><topic>Signal 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This method used the historical data aggregating normal working conditions of exhaust system, and found the principal component model of causality among variables in the process of expressing the normal working condition according to the statistical mode. If the real-time monitoring data did not correspond with the principal component model, we could determine the existence of fault in the exhaust system, then used change of each variable of measured data to analyze the variance contribution rate of principal component model, realizing fault diagnosis. Experimental result shows that the fault diagnosis noise of the proposed method is about 50dB lower than that of other methods, and the diagnostic stability and diagnosis rate are improved. Ji, W. and Li, B., 2018. Fault diagnosis method of exhaust system of port vehicle. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 469–473. 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| subjects | Algorithms Automation Batch processes Coasts Computer simulation Data Data processing Decomposition Diagnosis Diagnostic systems Electricity distribution exhaust system Exhaust systems Fault diagnosis History Methods Ocean engineering PORT ENGINEERING Port vehicle Ports Principal components analysis Science Signal processing Stability Variables Variance analysis Vehicle emissions Vehicles Working conditions |
| title | Fault Diagnosis Method of Exhaust System of Port Vehicle |
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