Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses
In this work, signal processing and pattern recognition techniques are combined to diagnose the severity of bearing faults. The signals were pre-processed by detrended-fluctuation analysis (DFA) and rescaled-range analysis (RSA) techniques and investigated by neural networks and principal components...
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| Veröffentlicht in: | Mechanical systems and signal processing 2011-07, Vol.25 (5), p.1765-1772 |
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| container_title | Mechanical systems and signal processing |
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| creator | de Moura, E.P. Souto, C.R. Silva, A.A. Irmão, M.A.S. |
| description | In this work, signal processing and pattern recognition techniques are combined to diagnose the severity of bearing faults. The signals were pre-processed by detrended-fluctuation analysis (DFA) and rescaled-range analysis (RSA) techniques and investigated by neural networks and principal components analysis in a total of four schemes. Three different levels of bearing fault severities together with a standard no-fault class were studied and compared. Signals were acquired from bearings working under different frequency and load conditions. An evaluation of fault recognition efficiency was performed for each combination of signal processing and pattern recognition techniques. All four schemes of classification yielded reasonably good results and are thus shown to be promising for rolling bearing fault monitoring and diagnosing. |
| doi_str_mv | 10.1016/j.ymssp.2010.11.021 |
| format | Article |
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The signals were pre-processed by detrended-fluctuation analysis (DFA) and rescaled-range analysis (RSA) techniques and investigated by neural networks and principal components analysis in a total of four schemes. Three different levels of bearing fault severities together with a standard no-fault class were studied and compared. Signals were acquired from bearings working under different frequency and load conditions. An evaluation of fault recognition efficiency was performed for each combination of signal processing and pattern recognition techniques. 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Machine design ; Monitoring ; Neural networks ; Pattern recognition ; Physics ; Principal component analysis ; Probability and statistics ; Roller bearings ; Sciences and techniques of general use ; Signal processing ; Solid mechanics ; Statistics ; Structural and continuum mechanics ; Vibration analysis ; Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...)</subject><ispartof>Mechanical systems and signal processing, 2011-07, Vol.25 (5), p.1765-1772</ispartof><rights>2010 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c410t-ee5230421cec10c6f7c71d230e837840a2403d0ff963ff3176e247e5ecf040533</citedby><cites>FETCH-LOGICAL-c410t-ee5230421cec10c6f7c71d230e837840a2403d0ff963ff3176e247e5ecf040533</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ymssp.2010.11.021$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24105437$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>de Moura, E.P.</creatorcontrib><creatorcontrib>Souto, C.R.</creatorcontrib><creatorcontrib>Silva, A.A.</creatorcontrib><creatorcontrib>Irmão, M.A.S.</creatorcontrib><title>Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses</title><title>Mechanical systems and signal processing</title><description>In this work, signal processing and pattern recognition techniques are combined to diagnose the severity of bearing faults. The signals were pre-processed by detrended-fluctuation analysis (DFA) and rescaled-range analysis (RSA) techniques and investigated by neural networks and principal components analysis in a total of four schemes. Three different levels of bearing fault severities together with a standard no-fault class were studied and compared. Signals were acquired from bearings working under different frequency and load conditions. An evaluation of fault recognition efficiency was performed for each combination of signal processing and pattern recognition techniques. All four schemes of classification yielded reasonably good results and are thus shown to be promising for rolling bearing fault monitoring and diagnosing.</description><subject>Applied sciences</subject><subject>Bearing</subject><subject>Bearings, bushings, rolling bearings</subject><subject>Detrended-fluctuation analysis</subject><subject>Drives</subject><subject>Exact sciences and technology</subject><subject>Fault diagnosis</subject><subject>Faults</subject><subject>Fundamental areas of phenomenology (including applications)</subject><subject>Hurst analysis</subject><subject>Industrial metrology. Testing</subject><subject>Inference from stochastic processes; time series analysis</subject><subject>Mathematics</subject><subject>Mechanical engineering. Machine design</subject><subject>Monitoring</subject><subject>Neural networks</subject><subject>Pattern recognition</subject><subject>Physics</subject><subject>Principal component analysis</subject><subject>Probability and statistics</subject><subject>Roller bearings</subject><subject>Sciences and techniques of general use</subject><subject>Signal processing</subject><subject>Solid mechanics</subject><subject>Statistics</subject><subject>Structural and continuum mechanics</subject><subject>Vibration analysis</subject><subject>Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...)</subject><issn>0888-3270</issn><issn>1096-1216</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9Uc2O0zAQthBIlIUn4OIL4pQytpM4PXBAyy6stBISP2fLdcaVS2IHT9JVn4WXxd1We-Tk0fj7mZmPsbcC1gJE-2G_Po5E01rCqSPWIMUzthKwaSshRfucraDrukpJDS_ZK6I9AGxqaFfs783BDoudQ4o8eT7lEF2Y7MBdGqcUMc7cRjscKVApeh5xyeU34vyQ8m8-YfYpjzY65KXgW7RFYce9XYaZ98HuYjpRfU4jP4RtPjtR2BXR4pYcEmHPt0f-_cejwefbiyHSa_bC24HwzeW9Yr9ub35ef63uv325u_50X7lawFwhNlJBLYVDJ8C1Xjst-tLCTumuBitrUD14v2mV90roFmWtsUHnoYZGqSv2_qxb5vmzIM1mDORwGGzEtJDpdAOi07otSHVGupyIMnpTDjbafDQCzCkJszePSZhTEkYIU5IorHcXfUvODj6XcwV6osqyRVMrXXAfzzgsyx4CZkMuYDltHzK62fQp_NfnH6mCoyk</recordid><startdate>20110701</startdate><enddate>20110701</enddate><creator>de Moura, E.P.</creator><creator>Souto, C.R.</creator><creator>Silva, A.A.</creator><creator>Irmão, M.A.S.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20110701</creationdate><title>Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses</title><author>de Moura, E.P. ; Souto, C.R. ; Silva, A.A. ; Irmão, M.A.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c410t-ee5230421cec10c6f7c71d230e837840a2403d0ff963ff3176e247e5ecf040533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Applied sciences</topic><topic>Bearing</topic><topic>Bearings, bushings, rolling bearings</topic><topic>Detrended-fluctuation analysis</topic><topic>Drives</topic><topic>Exact sciences and technology</topic><topic>Fault diagnosis</topic><topic>Faults</topic><topic>Fundamental areas of phenomenology (including applications)</topic><topic>Hurst analysis</topic><topic>Industrial metrology. Testing</topic><topic>Inference from stochastic processes; time series analysis</topic><topic>Mathematics</topic><topic>Mechanical engineering. Machine design</topic><topic>Monitoring</topic><topic>Neural networks</topic><topic>Pattern recognition</topic><topic>Physics</topic><topic>Principal component analysis</topic><topic>Probability and statistics</topic><topic>Roller bearings</topic><topic>Sciences and techniques of general use</topic><topic>Signal processing</topic><topic>Solid mechanics</topic><topic>Statistics</topic><topic>Structural and continuum mechanics</topic><topic>Vibration analysis</topic><topic>Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>de Moura, E.P.</creatorcontrib><creatorcontrib>Souto, C.R.</creatorcontrib><creatorcontrib>Silva, A.A.</creatorcontrib><creatorcontrib>Irmão, M.A.S.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Mechanical systems and signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>de Moura, E.P.</au><au>Souto, C.R.</au><au>Silva, A.A.</au><au>Irmão, M.A.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses</atitle><jtitle>Mechanical systems and signal processing</jtitle><date>2011-07-01</date><risdate>2011</risdate><volume>25</volume><issue>5</issue><spage>1765</spage><epage>1772</epage><pages>1765-1772</pages><issn>0888-3270</issn><eissn>1096-1216</eissn><abstract>In this work, signal processing and pattern recognition techniques are combined to diagnose the severity of bearing faults. 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| ispartof | Mechanical systems and signal processing, 2011-07, Vol.25 (5), p.1765-1772 |
| issn | 0888-3270 1096-1216 |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Applied sciences Bearing Bearings, bushings, rolling bearings Detrended-fluctuation analysis Drives Exact sciences and technology Fault diagnosis Faults Fundamental areas of phenomenology (including applications) Hurst analysis Industrial metrology. Testing Inference from stochastic processes time series analysis Mathematics Mechanical engineering. Machine design Monitoring Neural networks Pattern recognition Physics Principal component analysis Probability and statistics Roller bearings Sciences and techniques of general use Signal processing Solid mechanics Statistics Structural and continuum mechanics Vibration analysis Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...) |
| title | Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses |
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