A fault diagnosis approach for roller bearings based on EMD method and AR model
The main purpose of this paper is to propose a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings. AR model is an effective approach to extract the fault feature of the vibration signals and the fault pattern can...
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Veröffentlicht in: | Mechanical systems and signal processing 2006-02, Vol.20 (2), p.350-362 |
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creator | Junsheng, Cheng Dejie, Yu Yu, Yang |
description | The main purpose of this paper is to propose a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings. AR model is an effective approach to extract the fault feature of the vibration signals and the fault pattern can be identified directly by the extracted fault features without establishing the mathematical model and studying the fault mechanism of the system. However, AR model can only be applied to stationary signals, while the fault vibration signals of a roller bearing are non-stationary. Aiming at this problem, in this paper, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary, then the AR model of each IMF component can be established. The AR parameters and the remnant's variance of the AR models of each IMF components are regarded as the feature vectors. The
Mahalanobis
distance criterion function is used to identify the condition and fault pattern of a roller bearing. Experimental analysis results show that the roller bearing fault features can be extracted by the proposed approach effectively. |
doi_str_mv | 10.1016/j.ymssp.2004.11.002 |
format | Article |
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Mahalanobis
distance criterion function is used to identify the condition and fault pattern of a roller bearing. Experimental analysis results show that the roller bearing fault features can be extracted by the proposed approach effectively.</description><identifier>ISSN: 0888-3270</identifier><identifier>EISSN: 1096-1216</identifier><identifier>DOI: 10.1016/j.ymssp.2004.11.002</identifier><language>eng</language><publisher>London: Elsevier Ltd</publisher><subject>Applied sciences ; AR model ; Distance criterion function ; Drives ; EMD method ; Exact sciences and technology ; Fault diagnosis ; Fundamental areas of phenomenology (including applications) ; Gears ; Industrial metrology. Testing ; Measurement and testing methods ; Mechanical engineering. Machine design ; Physics ; Roller bearings ; Solid mechanics ; Structural and continuum mechanics</subject><ispartof>Mechanical systems and signal processing, 2006-02, Vol.20 (2), p.350-362</ispartof><rights>2004 Elsevier Ltd</rights><rights>2006 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-5eca5e75849e73cb04642ea91af6439a0b60010ba29cf3aef084b7d9f912793</citedby><cites>FETCH-LOGICAL-c364t-5eca5e75849e73cb04642ea91af6439a0b60010ba29cf3aef084b7d9f912793</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0888327004001736$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17555177$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Junsheng, Cheng</creatorcontrib><creatorcontrib>Dejie, Yu</creatorcontrib><creatorcontrib>Yu, Yang</creatorcontrib><title>A fault diagnosis approach for roller bearings based on EMD method and AR model</title><title>Mechanical systems and signal processing</title><description>The main purpose of this paper is to propose a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings. AR model is an effective approach to extract the fault feature of the vibration signals and the fault pattern can be identified directly by the extracted fault features without establishing the mathematical model and studying the fault mechanism of the system. However, AR model can only be applied to stationary signals, while the fault vibration signals of a roller bearing are non-stationary. Aiming at this problem, in this paper, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary, then the AR model of each IMF component can be established. The AR parameters and the remnant's variance of the AR models of each IMF components are regarded as the feature vectors. The
Mahalanobis
distance criterion function is used to identify the condition and fault pattern of a roller bearing. Experimental analysis results show that the roller bearing fault features can be extracted by the proposed approach effectively.</description><subject>Applied sciences</subject><subject>AR model</subject><subject>Distance criterion function</subject><subject>Drives</subject><subject>EMD method</subject><subject>Exact sciences and technology</subject><subject>Fault diagnosis</subject><subject>Fundamental areas of phenomenology (including applications)</subject><subject>Gears</subject><subject>Industrial metrology. Testing</subject><subject>Measurement and testing methods</subject><subject>Mechanical engineering. Machine design</subject><subject>Physics</subject><subject>Roller bearings</subject><subject>Solid mechanics</subject><subject>Structural and continuum mechanics</subject><issn>0888-3270</issn><issn>1096-1216</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNp9kE1v1DAQhi0EEkvhF3DxBW4JY8eJ4wOHVSkfUlEl4G5NnHHrVRIvnmyl_ntSthI3TnN53ndmHiHeKqgVqO7DoX6YmY-1BjC1UjWAfiZ2ClxXKa2652IHfd9XjbbwUrxiPgCAM9DtxM1eRjxNqxwT3i6ZE0s8HkvGcCdjLrLkaaIiB8KSlluWAzKNMi_y6vsnOdN6l0eJyyj3P-ScR5peixcRJ6Y3T_NC_Px89evya3V98-Xb5f66Ck1n1qqlgC3ZtjeObBMGMJ3RhE5h7EzjEIYOQMGA2oXYIEXozWBHF53S1jUX4v25dbv094l49XPiQNOEC-UTe-10a2yrN7A5g6Fk5kLRH0uasTx4Bf5RnT_4v-r8ozqvlN_Ubal3T_XIAadYcAmJ_0Vt27bK2o37eOZo-_Q-UfEcEi2BxlQorH7M6b97_gAZ34S9</recordid><startdate>20060201</startdate><enddate>20060201</enddate><creator>Junsheng, Cheng</creator><creator>Dejie, Yu</creator><creator>Yu, Yang</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>20060201</creationdate><title>A fault diagnosis approach for roller bearings based on EMD method and AR model</title><author>Junsheng, Cheng ; Dejie, Yu ; Yu, Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-5eca5e75849e73cb04642ea91af6439a0b60010ba29cf3aef084b7d9f912793</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>AR model</topic><topic>Distance criterion function</topic><topic>Drives</topic><topic>EMD method</topic><topic>Exact sciences and technology</topic><topic>Fault diagnosis</topic><topic>Fundamental areas of phenomenology (including applications)</topic><topic>Gears</topic><topic>Industrial metrology. Testing</topic><topic>Measurement and testing methods</topic><topic>Mechanical engineering. Machine design</topic><topic>Physics</topic><topic>Roller bearings</topic><topic>Solid mechanics</topic><topic>Structural and continuum mechanics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Junsheng, Cheng</creatorcontrib><creatorcontrib>Dejie, Yu</creatorcontrib><creatorcontrib>Yu, Yang</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>Junsheng, Cheng</au><au>Dejie, Yu</au><au>Yu, Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A fault diagnosis approach for roller bearings based on EMD method and AR model</atitle><jtitle>Mechanical systems and signal processing</jtitle><date>2006-02-01</date><risdate>2006</risdate><volume>20</volume><issue>2</issue><spage>350</spage><epage>362</epage><pages>350-362</pages><issn>0888-3270</issn><eissn>1096-1216</eissn><abstract>The main purpose of this paper is to propose a new fault feature extraction approach based on empirical mode decomposition (EMD) method and autoregressive (AR) model for roller bearings. AR model is an effective approach to extract the fault feature of the vibration signals and the fault pattern can be identified directly by the extracted fault features without establishing the mathematical model and studying the fault mechanism of the system. However, AR model can only be applied to stationary signals, while the fault vibration signals of a roller bearing are non-stationary. Aiming at this problem, in this paper, the EMD method is used as a pretreatment to decompose the non-stationary vibration signal of a roller bearing into a number of intrinsic mode function (IMF) components which are stationary, then the AR model of each IMF component can be established. The AR parameters and the remnant's variance of the AR models of each IMF components are regarded as the feature vectors. The
Mahalanobis
distance criterion function is used to identify the condition and fault pattern of a roller bearing. Experimental analysis results show that the roller bearing fault features can be extracted by the proposed approach effectively.</abstract><cop>London</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ymssp.2004.11.002</doi><tpages>13</tpages></addata></record> |
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subjects | Applied sciences AR model Distance criterion function Drives EMD method Exact sciences and technology Fault diagnosis Fundamental areas of phenomenology (including applications) Gears Industrial metrology. Testing Measurement and testing methods Mechanical engineering. Machine design Physics Roller bearings Solid mechanics Structural and continuum mechanics |
title | A fault diagnosis approach for roller bearings based on EMD method and AR model |
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