Engine Condition Monitoring Based on Grey AR Combination Model
Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be r...
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| creator | Wang, Qiang Dai, Sheng Hui |
| description | Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result. |
| doi_str_mv | 10.1109/CESCE.2010.19 |
| format | Conference Proceeding |
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By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result.</description><identifier>ISBN: 1424459230</identifier><identifier>ISBN: 9780769539720</identifier><identifier>ISBN: 9781424459230</identifier><identifier>ISBN: 0769539726</identifier><identifier>EISBN: 1424459249</identifier><identifier>EISBN: 9781424459247</identifier><identifier>DOI: 10.1109/CESCE.2010.19</identifier><identifier>LCCN: 2010900624</identifier><language>eng</language><publisher>IEEE</publisher><subject>Abrasives ; auto-regressive ; Condition monitoring ; Engines ; Graphics ; grey theory ; Least squares approximation ; Linear regression ; Predictive models ; Technology forecasting ; Testing ; Time series analysis</subject><ispartof>2010 International Conference on Challenges in Environmental Science and Computer Engineering, 2010, Vol.1, p.215-218</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/5493095$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,2052,27906,54901</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5493095$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Qiang</creatorcontrib><creatorcontrib>Dai, Sheng Hui</creatorcontrib><title>Engine Condition Monitoring Based on Grey AR Combination Model</title><title>2010 International Conference on Challenges in Environmental Science and Computer Engineering</title><addtitle>CESCE</addtitle><description>Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result.</description><subject>Abrasives</subject><subject>auto-regressive</subject><subject>Condition monitoring</subject><subject>Engines</subject><subject>Graphics</subject><subject>grey theory</subject><subject>Least squares approximation</subject><subject>Linear regression</subject><subject>Predictive models</subject><subject>Technology forecasting</subject><subject>Testing</subject><subject>Time series analysis</subject><isbn>1424459230</isbn><isbn>9780769539720</isbn><isbn>9781424459230</isbn><isbn>0769539726</isbn><isbn>1424459249</isbn><isbn>9781424459247</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFjE1Lw0AURUekoK1dunIzfyD1vfkybyPUEKtQEbT7MnHelJF2Ikk2_femWPBuLvdwuELcIiwQge6r-rOqFwpOmy7EFI0yxpIydPk_NEzE9OQQgFPmSsz7_hvGOFeWDq_FY513KbOs2hzSkNos39qchrZLeSeffM9BjmzV8VEuP0br0KTsz17g_Y2YRL_veX7umdg815vqpVi_r16r5bpIBEPBDWmnSCE33lnyHFWAWHpgE9jqBpUHA8Tk8QtHWBJgiMZbfLDOcNQzcfd3m5h5-9Olg--OW2tIA1n9C_zaSTQ</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Wang, Qiang</creator><creator>Dai, Sheng Hui</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201003</creationdate><title>Engine Condition Monitoring Based on Grey AR Combination Model</title><author>Wang, Qiang ; Dai, Sheng Hui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-eb9362921eba659aef2d0f8a0e4de53b12a0409e9a1c10e48901df4a517564ef3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Abrasives</topic><topic>auto-regressive</topic><topic>Condition monitoring</topic><topic>Engines</topic><topic>Graphics</topic><topic>grey theory</topic><topic>Least squares approximation</topic><topic>Linear regression</topic><topic>Predictive models</topic><topic>Technology forecasting</topic><topic>Testing</topic><topic>Time series analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Qiang</creatorcontrib><creatorcontrib>Dai, Sheng Hui</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>Wang, Qiang</au><au>Dai, Sheng Hui</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Engine Condition Monitoring Based on Grey AR Combination Model</atitle><btitle>2010 International Conference on Challenges in Environmental Science and Computer Engineering</btitle><stitle>CESCE</stitle><date>2010-03</date><risdate>2010</risdate><volume>1</volume><spage>215</spage><epage>218</epage><pages>215-218</pages><isbn>1424459230</isbn><isbn>9780769539720</isbn><isbn>9781424459230</isbn><isbn>0769539726</isbn><eisbn>1424459249</eisbn><eisbn>9781424459247</eisbn><abstract>Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result.</abstract><pub>IEEE</pub><doi>10.1109/CESCE.2010.19</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Abrasives auto-regressive Condition monitoring Engines Graphics grey theory Least squares approximation Linear regression Predictive models Technology forecasting Testing Time series analysis |
| title | Engine Condition Monitoring Based on Grey AR Combination Model |
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