Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks
This paper proposes a new approach for rotating machinery which integrates wavelet transform (WT), principal component analysis (PCA), and artificial neural networks (ANN) to classify the fault and predict the conditions of components, equipment, and machines. The standard deviation of wavelet coeff...
Gespeichert in:
Veröffentlicht in: | International journal of advanced manufacturing technology 2013-09, Vol.68 (1-4), p.763-773 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 773 |
---|---|
container_issue | 1-4 |
container_start_page | 763 |
container_title | International journal of advanced manufacturing technology |
container_volume | 68 |
creator | Zhang, Zhenyou Wang, Yi Wang, Kesheng |
description | This paper proposes a new approach for rotating machinery which integrates wavelet transform (WT), principal component analysis (PCA), and artificial neural networks (ANN) to classify the fault and predict the conditions of components, equipment, and machines. The standard deviation of wavelet coefficients are extracted from processed historical signals of manufacturing equipment as features. Then, the features are analyzed by PCA and several new principal features obtained from original features can be used as inputs to train ANN. After training, the conditions and degradations of components and machines can be predicted, and the fault of them can be classified if it exists, by the trained ANN using the same kinds of principal features extracted from real time signals. A case study is used to evaluate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods. |
doi_str_mv | 10.1007/s00170-013-4797-0 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2262369480</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2262369480</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-b73f4c0362b445bd964ef50a7bb1294a650b131ee2a44e9d46b501c8615b762b3</originalsourceid><addsrcrecordid>eNp1kc1OxCAUhYnRxPHnAdyRuB0UCqXt0kz8mWQSN7omtKWVsYUK1Mk8kO8pnWpcubpwc853uRwArgi-IRhntx5jkmGECUUsKzKEj8CCMEoRxSQ9Bguc8BzRjOen4Mz7bVRzwvMF-FqboLpOt8oE2MixC7DWsjXWaw-lqeHg7O9tiGdZvcHGOuhskEGbFvaxo41ye6gjqXVzdyc_VacCDE4aH_X9MoK0qfQgO1jZfrBmGiiN7PaRvTyMki7oRlc6Sowa3aGEnXXv_gKcNLLz6vKnnoPXh_uX1RPaPD-uV3cbVFHCAyoz2rAKU56UjKVlXXCmmhTLrCxJUjDJU1wSSpRKJGOqqBkvU0yqnJO0zKKJnoPrmRs3_RiVD2JrRxcf6UWS8ITyguU4qsisqpz13qlGxN166faCYDGlIeY0RExDTGmIyZPMHj_9Q6vcH_l_0zdDUZGl</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2262369480</pqid></control><display><type>article</type><title>Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks</title><source>Springer Nature - Complete Springer Journals</source><creator>Zhang, Zhenyou ; Wang, Yi ; Wang, Kesheng</creator><creatorcontrib>Zhang, Zhenyou ; Wang, Yi ; Wang, Kesheng</creatorcontrib><description>This paper proposes a new approach for rotating machinery which integrates wavelet transform (WT), principal component analysis (PCA), and artificial neural networks (ANN) to classify the fault and predict the conditions of components, equipment, and machines. The standard deviation of wavelet coefficients are extracted from processed historical signals of manufacturing equipment as features. Then, the features are analyzed by PCA and several new principal features obtained from original features can be used as inputs to train ANN. After training, the conditions and degradations of components and machines can be predicted, and the fault of them can be classified if it exists, by the trained ANN using the same kinds of principal features extracted from real time signals. A case study is used to evaluate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods.</description><identifier>ISSN: 0268-3768</identifier><identifier>EISSN: 1433-3015</identifier><identifier>DOI: 10.1007/s00170-013-4797-0</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Artificial neural networks ; CAE) and Design ; Computer-Aided Engineering (CAD ; Engineering ; Fault diagnosis ; Feature extraction ; Industrial and Production Engineering ; Mechanical Engineering ; Media Management ; Neural networks ; Original Article ; Principal components analysis ; Rotating machinery ; Rotation ; Signal processing ; Time signals ; Wavelet analysis ; Wavelet transforms</subject><ispartof>International journal of advanced manufacturing technology, 2013-09, Vol.68 (1-4), p.763-773</ispartof><rights>Springer-Verlag London 2013</rights><rights>The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2013). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-b73f4c0362b445bd964ef50a7bb1294a650b131ee2a44e9d46b501c8615b762b3</citedby><cites>FETCH-LOGICAL-c316t-b73f4c0362b445bd964ef50a7bb1294a650b131ee2a44e9d46b501c8615b762b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00170-013-4797-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00170-013-4797-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,778,782,27911,27912,41475,42544,51306</link.rule.ids></links><search><creatorcontrib>Zhang, Zhenyou</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Wang, Kesheng</creatorcontrib><title>Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks</title><title>International journal of advanced manufacturing technology</title><addtitle>Int J Adv Manuf Technol</addtitle><description>This paper proposes a new approach for rotating machinery which integrates wavelet transform (WT), principal component analysis (PCA), and artificial neural networks (ANN) to classify the fault and predict the conditions of components, equipment, and machines. The standard deviation of wavelet coefficients are extracted from processed historical signals of manufacturing equipment as features. Then, the features are analyzed by PCA and several new principal features obtained from original features can be used as inputs to train ANN. After training, the conditions and degradations of components and machines can be predicted, and the fault of them can be classified if it exists, by the trained ANN using the same kinds of principal features extracted from real time signals. A case study is used to evaluate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods.</description><subject>Artificial neural networks</subject><subject>CAE) and Design</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Engineering</subject><subject>Fault diagnosis</subject><subject>Feature extraction</subject><subject>Industrial and Production Engineering</subject><subject>Mechanical Engineering</subject><subject>Media Management</subject><subject>Neural networks</subject><subject>Original Article</subject><subject>Principal components analysis</subject><subject>Rotating machinery</subject><subject>Rotation</subject><subject>Signal processing</subject><subject>Time signals</subject><subject>Wavelet analysis</subject><subject>Wavelet transforms</subject><issn>0268-3768</issn><issn>1433-3015</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kc1OxCAUhYnRxPHnAdyRuB0UCqXt0kz8mWQSN7omtKWVsYUK1Mk8kO8pnWpcubpwc853uRwArgi-IRhntx5jkmGECUUsKzKEj8CCMEoRxSQ9Bguc8BzRjOen4Mz7bVRzwvMF-FqboLpOt8oE2MixC7DWsjXWaw-lqeHg7O9tiGdZvcHGOuhskEGbFvaxo41ye6gjqXVzdyc_VacCDE4aH_X9MoK0qfQgO1jZfrBmGiiN7PaRvTyMki7oRlc6Sowa3aGEnXXv_gKcNLLz6vKnnoPXh_uX1RPaPD-uV3cbVFHCAyoz2rAKU56UjKVlXXCmmhTLrCxJUjDJU1wSSpRKJGOqqBkvU0yqnJO0zKKJnoPrmRs3_RiVD2JrRxcf6UWS8ITyguU4qsisqpz13qlGxN166faCYDGlIeY0RExDTGmIyZPMHj_9Q6vcH_l_0zdDUZGl</recordid><startdate>20130901</startdate><enddate>20130901</enddate><creator>Zhang, Zhenyou</creator><creator>Wang, Yi</creator><creator>Wang, Kesheng</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20130901</creationdate><title>Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks</title><author>Zhang, Zhenyou ; Wang, Yi ; Wang, Kesheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-b73f4c0362b445bd964ef50a7bb1294a650b131ee2a44e9d46b501c8615b762b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Artificial neural networks</topic><topic>CAE) and Design</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Engineering</topic><topic>Fault diagnosis</topic><topic>Feature extraction</topic><topic>Industrial and Production Engineering</topic><topic>Mechanical Engineering</topic><topic>Media Management</topic><topic>Neural networks</topic><topic>Original Article</topic><topic>Principal components analysis</topic><topic>Rotating machinery</topic><topic>Rotation</topic><topic>Signal processing</topic><topic>Time signals</topic><topic>Wavelet analysis</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Zhenyou</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Wang, Kesheng</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</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>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><jtitle>International journal of advanced manufacturing technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Zhenyou</au><au>Wang, Yi</au><au>Wang, Kesheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks</atitle><jtitle>International journal of advanced manufacturing technology</jtitle><stitle>Int J Adv Manuf Technol</stitle><date>2013-09-01</date><risdate>2013</risdate><volume>68</volume><issue>1-4</issue><spage>763</spage><epage>773</epage><pages>763-773</pages><issn>0268-3768</issn><eissn>1433-3015</eissn><abstract>This paper proposes a new approach for rotating machinery which integrates wavelet transform (WT), principal component analysis (PCA), and artificial neural networks (ANN) to classify the fault and predict the conditions of components, equipment, and machines. The standard deviation of wavelet coefficients are extracted from processed historical signals of manufacturing equipment as features. Then, the features are analyzed by PCA and several new principal features obtained from original features can be used as inputs to train ANN. After training, the conditions and degradations of components and machines can be predicted, and the fault of them can be classified if it exists, by the trained ANN using the same kinds of principal features extracted from real time signals. A case study is used to evaluate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00170-013-4797-0</doi><tpages>11</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0268-3768 |
ispartof | International journal of advanced manufacturing technology, 2013-09, Vol.68 (1-4), p.763-773 |
issn | 0268-3768 1433-3015 |
language | eng |
recordid | cdi_proquest_journals_2262369480 |
source | Springer Nature - Complete Springer Journals |
subjects | Artificial neural networks CAE) and Design Computer-Aided Engineering (CAD Engineering Fault diagnosis Feature extraction Industrial and Production Engineering Mechanical Engineering Media Management Neural networks Original Article Principal components analysis Rotating machinery Rotation Signal processing Time signals Wavelet analysis Wavelet transforms |
title | Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T05%3A48%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Intelligent%20fault%20diagnosis%20and%20prognosis%20approach%20for%20rotating%20machinery%20integrating%20wavelet%20transform,%20principal%20component%20analysis,%20and%20artificial%20neural%20networks&rft.jtitle=International%20journal%20of%20advanced%20manufacturing%20technology&rft.au=Zhang,%20Zhenyou&rft.date=2013-09-01&rft.volume=68&rft.issue=1-4&rft.spage=763&rft.epage=773&rft.pages=763-773&rft.issn=0268-3768&rft.eissn=1433-3015&rft_id=info:doi/10.1007/s00170-013-4797-0&rft_dat=%3Cproquest_cross%3E2262369480%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2262369480&rft_id=info:pmid/&rfr_iscdi=true |