A new method for the fault diagnosis of the train wheelset based on characteristic spectrum analysis
In the view of the fact that the information of the fault characteristic of the train wheelset is submerged into the background noises and the conventional spectral analysis method has its own deficiency due to the fuzzy spectrum value created by the load variation and rotation speed fluctuation, a...
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| creator | Zhang, Jian Zhou, Shaowu Huang, Cailun |
| description | In the view of the fact that the information of the fault characteristic of the train wheelset is submerged into the background noises and the conventional spectral analysis method has its own deficiency due to the fuzzy spectrum value created by the load variation and rotation speed fluctuation, a method of full period and uniform angle sampling is applied, which transfers the vibration signal from time domain into angular domain. The angular domain signal is transferred into corresponding characteristic spectrum using FFT. Through spectrum estimation and analysis, fault characteristic spectrum values of train wheelset components are acquired to distinguish their faults. The application indicates the method can distinguish the faults of train wheelset accurately and efficiently. |
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The angular domain signal is transferred into corresponding characteristic spectrum using FFT. Through spectrum estimation and analysis, fault characteristic spectrum values of train wheelset components are acquired to distinguish their faults. The application indicates the method can distinguish the faults of train wheelset accurately and efficiently.</description><identifier>ISSN: 1934-1768</identifier><identifier>ISBN: 9781424462636</identifier><identifier>ISBN: 1424462630</identifier><identifier>EISSN: 2161-2927</identifier><identifier>EISBN: 9787894631046</identifier><identifier>EISBN: 7894631043</identifier><language>eng</language><publisher>IEEE</publisher><subject>Bearing ; Characteristic Spectrum Analysis ; Circuit faults ; Eigenvalues and eigenfunctions ; Fault diagnosis ; Full Period and Uniform Angle Sampling ; Monitoring ; Spectral analysis ; Time frequency analysis ; Vibrations ; Wheelset</subject><ispartof>Proceedings of the 29th Chinese Control Conference, 2010, p.3988-3992</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5572987$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,54899</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5572987$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhang, Jian</creatorcontrib><creatorcontrib>Zhou, Shaowu</creatorcontrib><creatorcontrib>Huang, Cailun</creatorcontrib><title>A new method for the fault diagnosis of the train wheelset based on characteristic spectrum analysis</title><title>Proceedings of the 29th Chinese Control Conference</title><addtitle>CHICC</addtitle><description>In the view of the fact that the information of the fault characteristic of the train wheelset is submerged into the background noises and the conventional spectral analysis method has its own deficiency due to the fuzzy spectrum value created by the load variation and rotation speed fluctuation, a method of full period and uniform angle sampling is applied, which transfers the vibration signal from time domain into angular domain. The angular domain signal is transferred into corresponding characteristic spectrum using FFT. Through spectrum estimation and analysis, fault characteristic spectrum values of train wheelset components are acquired to distinguish their faults. The application indicates the method can distinguish the faults of train wheelset accurately and efficiently.</description><subject>Bearing</subject><subject>Characteristic Spectrum Analysis</subject><subject>Circuit faults</subject><subject>Eigenvalues and eigenfunctions</subject><subject>Fault diagnosis</subject><subject>Full Period and Uniform Angle Sampling</subject><subject>Monitoring</subject><subject>Spectral analysis</subject><subject>Time frequency analysis</subject><subject>Vibrations</subject><subject>Wheelset</subject><issn>1934-1768</issn><issn>2161-2927</issn><isbn>9781424462636</isbn><isbn>1424462630</isbn><isbn>9787894631046</isbn><isbn>7894631043</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9i8GKwjAUReOoMEX7BbN5P1Bo0jRpliKKHzB7ebavNtImkkTEv58iruduDtzDWbDc6EY3RqqKl1J9sUxwxQthhF6-HZdCSiVUpVYs46aSBdeq-WZ5jLdynqyF5mXGuh04esJEafAd9D5AGgh6fIwJOotX56ON4Pv3nQJaB8-BaIyU4IKROvAO2gEDtomCjcm2EO_UpvCYAB2Or7nfsnWPc5J_uGE_x8Pv_lRYIjrfg50wvM51rYVpdPW__QM16Egm</recordid><startdate>201007</startdate><enddate>201007</enddate><creator>Zhang, Jian</creator><creator>Zhou, Shaowu</creator><creator>Huang, Cailun</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201007</creationdate><title>A new method for the fault diagnosis of the train wheelset based on characteristic spectrum analysis</title><author>Zhang, Jian ; Zhou, Shaowu ; Huang, Cailun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_55729873</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Bearing</topic><topic>Characteristic Spectrum Analysis</topic><topic>Circuit faults</topic><topic>Eigenvalues and eigenfunctions</topic><topic>Fault diagnosis</topic><topic>Full Period and Uniform Angle Sampling</topic><topic>Monitoring</topic><topic>Spectral analysis</topic><topic>Time frequency analysis</topic><topic>Vibrations</topic><topic>Wheelset</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Jian</creatorcontrib><creatorcontrib>Zhou, Shaowu</creatorcontrib><creatorcontrib>Huang, Cailun</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>Zhang, Jian</au><au>Zhou, Shaowu</au><au>Huang, Cailun</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A new method for the fault diagnosis of the train wheelset based on characteristic spectrum analysis</atitle><btitle>Proceedings of the 29th Chinese Control Conference</btitle><stitle>CHICC</stitle><date>2010-07</date><risdate>2010</risdate><spage>3988</spage><epage>3992</epage><pages>3988-3992</pages><issn>1934-1768</issn><eissn>2161-2927</eissn><isbn>9781424462636</isbn><isbn>1424462630</isbn><eisbn>9787894631046</eisbn><eisbn>7894631043</eisbn><abstract>In the view of the fact that the information of the fault characteristic of the train wheelset is submerged into the background noises and the conventional spectral analysis method has its own deficiency due to the fuzzy spectrum value created by the load variation and rotation speed fluctuation, a method of full period and uniform angle sampling is applied, which transfers the vibration signal from time domain into angular domain. The angular domain signal is transferred into corresponding characteristic spectrum using FFT. Through spectrum estimation and analysis, fault characteristic spectrum values of train wheelset components are acquired to distinguish their faults. The application indicates the method can distinguish the faults of train wheelset accurately and efficiently.</abstract><pub>IEEE</pub></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bearing Characteristic Spectrum Analysis Circuit faults Eigenvalues and eigenfunctions Fault diagnosis Full Period and Uniform Angle Sampling Monitoring Spectral analysis Time frequency analysis Vibrations Wheelset |
| title | A new method for the fault diagnosis of the train wheelset based on characteristic spectrum analysis |
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