Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM
Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying fea- tures. To investigate the fault diagnosis of diesel engines, fractal correlati...
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| Veröffentlicht in: | Chinese journal of mechanical engineering 2017-07, Vol.30 (4), p.991-1007 |
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| creator | Jing, Ya-Bing Liu, Chang-Wen Bi, Feng-Rong Bi, Xiao-Yang Wang, Xia Shao, Kang |
| description | Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying fea- tures. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastlCA-SVM achieves higher classification accuracy and makes better general- ization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastlCA- SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of fea- ture extraction and the fault diagnosis of diesel engines. |
| doi_str_mv | 10.1007/s10033-017-0140-2 |
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To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastlCA-SVM achieves higher classification accuracy and makes better general- ization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastlCA- SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of fea- ture extraction and the fault diagnosis of diesel engines.</description><edition>English ed.</edition><identifier>ISSN: 1000-9345</identifier><identifier>EISSN: 2192-8258</identifier><identifier>DOI: 10.1007/s10033-017-0140-2</identifier><language>eng</language><publisher>Beijing: Chinese Mechanical Engineering Society</publisher><subject>Acceleration ; Automotive parts ; Classification ; Cylinder heads ; Diesel engines ; Electrical Machines and Networks ; Electronics and Microelectronics ; Engine valves ; Engineering ; Engineering Thermodynamics ; Engines ; Entropy ; FastICA算法 ; Fault diagnosis ; Feature extraction ; Fractals ; Heat and Mass Transfer ; Instrumentation ; Machines ; Manufacturing ; Mechanical Engineering ; Original Article ; Power Electronics ; Processes ; Theoretical and Applied Mechanics ; Valve trains ; Vibration analysis ; Wavelet analysis ; 基于特征 ; 提取技术 ; 支持向量机算法 ; 故障诊断技术 ; 柴油机故障 ; 气门间隙 ; 表面振动信号</subject><ispartof>Chinese journal of mechanical engineering, 2017-07, Vol.30 (4), p.991-1007</ispartof><rights>Chinese Mechanical Engineering Society and Springer-Verlag Berlin Heidelberg 2017</rights><rights>Chinese Journal of Mechanical Engineering is a copyright of Springer, (2017). All Rights Reserved.</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c377t-955dc941d653a847e207c95965ac7b055f3d69a9858962da894ae68992ad9f363</citedby><cites>FETCH-LOGICAL-c377t-955dc941d653a847e207c95965ac7b055f3d69a9858962da894ae68992ad9f363</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/85891X/85891X.jpg</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Jing, Ya-Bing</creatorcontrib><creatorcontrib>Liu, Chang-Wen</creatorcontrib><creatorcontrib>Bi, Feng-Rong</creatorcontrib><creatorcontrib>Bi, Xiao-Yang</creatorcontrib><creatorcontrib>Wang, Xia</creatorcontrib><creatorcontrib>Shao, Kang</creatorcontrib><title>Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM</title><title>Chinese journal of mechanical engineering</title><addtitle>Chin. J. Mech. Eng</addtitle><addtitle>Chinese Journal of Mechanical Engineering</addtitle><description>Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying fea- tures. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastlCA-SVM achieves higher classification accuracy and makes better general- ization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastlCA- SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of fea- ture extraction and the fault diagnosis of diesel engines.</description><subject>Acceleration</subject><subject>Automotive parts</subject><subject>Classification</subject><subject>Cylinder heads</subject><subject>Diesel engines</subject><subject>Electrical Machines and Networks</subject><subject>Electronics and Microelectronics</subject><subject>Engine valves</subject><subject>Engineering</subject><subject>Engineering Thermodynamics</subject><subject>Engines</subject><subject>Entropy</subject><subject>FastICA算法</subject><subject>Fault diagnosis</subject><subject>Feature extraction</subject><subject>Fractals</subject><subject>Heat and Mass Transfer</subject><subject>Instrumentation</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mechanical Engineering</subject><subject>Original Article</subject><subject>Power Electronics</subject><subject>Processes</subject><subject>Theoretical and Applied Mechanics</subject><subject>Valve trains</subject><subject>Vibration analysis</subject><subject>Wavelet analysis</subject><subject>基于特征</subject><subject>提取技术</subject><subject>支持向量机算法</subject><subject>故障诊断技术</subject><subject>柴油机故障</subject><subject>气门间隙</subject><subject>表面振动信号</subject><issn>1000-9345</issn><issn>2192-8258</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kEtPxCAUhYnRxHH0B7gjunJR5VEKLHUeaqJx4WNLrpTWTiqdgY6O_14mNbpzAYTLd84hB6FjSs4pIfIipp3zjFCZVk4ytoNGjGqWKSbULhqlZ5Jpnot9dBDjIt0KStUIuWnjomvxzNeNd_gF2g-HJ62DAN46PId12-NpA7XvYhPxFURX4s7juYN-HVzEs00fwPZNmj05--ab1TpNwZdJG_vbyWX2-HJ_iPYqaKM7-jnH6Hk-e5rcZHcP1wm5yyyXss-0EKXVOS0LwUHl0jEirRa6EGDlKxGi4mWhQSuhdMFKUDoHVyitGZS64gUfo7PB9xN8Bb42i24dfEo0i01tN68mOVJJcsJYYk8Hdhm67Z_7P5gxobliitBE0YGyoYsxuMosQ_MO4ctQYrbNm6F5k3zNtnmzdWaDJibW1y78Of8nOvkJeut8vUq636RCMilkzgX_BrYojw0</recordid><startdate>20170701</startdate><enddate>20170701</enddate><creator>Jing, Ya-Bing</creator><creator>Liu, Chang-Wen</creator><creator>Bi, Feng-Rong</creator><creator>Bi, Xiao-Yang</creator><creator>Wang, Xia</creator><creator>Shao, Kang</creator><general>Chinese Mechanical Engineering Society</general><general>Springer Nature B.V</general><general>State Key Laboratory of Engines, Tianjin University,Tianjin 300072, China</general><general>Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China%State Key Laboratory of Engines, Tianjin University,Tianjin 300072, China%School of Mechanical Engineering, Tianjin University,Tianjin 300072, China</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20170701</creationdate><title>Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM</title><author>Jing, Ya-Bing ; Liu, Chang-Wen ; Bi, Feng-Rong ; Bi, Xiao-Yang ; Wang, Xia ; Shao, Kang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c377t-955dc941d653a847e207c95965ac7b055f3d69a9858962da894ae68992ad9f363</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Acceleration</topic><topic>Automotive parts</topic><topic>Classification</topic><topic>Cylinder heads</topic><topic>Diesel engines</topic><topic>Electrical Machines and Networks</topic><topic>Electronics and Microelectronics</topic><topic>Engine valves</topic><topic>Engineering</topic><topic>Engineering Thermodynamics</topic><topic>Engines</topic><topic>Entropy</topic><topic>FastICA算法</topic><topic>Fault diagnosis</topic><topic>Feature extraction</topic><topic>Fractals</topic><topic>Heat and Mass Transfer</topic><topic>Instrumentation</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Mechanical Engineering</topic><topic>Original Article</topic><topic>Power Electronics</topic><topic>Processes</topic><topic>Theoretical and Applied Mechanics</topic><topic>Valve trains</topic><topic>Vibration analysis</topic><topic>Wavelet analysis</topic><topic>基于特征</topic><topic>提取技术</topic><topic>支持向量机算法</topic><topic>故障诊断技术</topic><topic>柴油机故障</topic><topic>气门间隙</topic><topic>表面振动信号</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jing, Ya-Bing</creatorcontrib><creatorcontrib>Liu, Chang-Wen</creatorcontrib><creatorcontrib>Bi, Feng-Rong</creatorcontrib><creatorcontrib>Bi, Xiao-Yang</creatorcontrib><creatorcontrib>Wang, Xia</creatorcontrib><creatorcontrib>Shao, Kang</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Chinese journal of mechanical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jing, Ya-Bing</au><au>Liu, Chang-Wen</au><au>Bi, Feng-Rong</au><au>Bi, Xiao-Yang</au><au>Wang, Xia</au><au>Shao, Kang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM</atitle><jtitle>Chinese journal of mechanical engineering</jtitle><stitle>Chin. J. Mech. Eng</stitle><addtitle>Chinese Journal of Mechanical Engineering</addtitle><date>2017-07-01</date><risdate>2017</risdate><volume>30</volume><issue>4</issue><spage>991</spage><epage>1007</epage><pages>991-1007</pages><issn>1000-9345</issn><eissn>2192-8258</eissn><abstract>Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying fea- tures. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastlCA-SVM achieves higher classification accuracy and makes better general- ization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastlCA- SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of fea- ture extraction and the fault diagnosis of diesel engines.</abstract><cop>Beijing</cop><pub>Chinese Mechanical Engineering Society</pub><doi>10.1007/s10033-017-0140-2</doi><tpages>17</tpages><edition>English ed.</edition><oa>free_for_read</oa></addata></record> |
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| ispartof | Chinese journal of mechanical engineering, 2017-07, Vol.30 (4), p.991-1007 |
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| subjects | Acceleration Automotive parts Classification Cylinder heads Diesel engines Electrical Machines and Networks Electronics and Microelectronics Engine valves Engineering Engineering Thermodynamics Engines Entropy FastICA算法 Fault diagnosis Feature extraction Fractals Heat and Mass Transfer Instrumentation Machines Manufacturing Mechanical Engineering Original Article Power Electronics Processes Theoretical and Applied Mechanics Valve trains Vibration analysis Wavelet analysis 基于特征 提取技术 支持向量机算法 故障诊断技术 柴油机故障 气门间隙 表面振动信号 |
| title | Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM |
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