Pattern recognition in hydraulic backlash using neural network

An approach for estimating and classifying backlash clearance fault condition in hydraulic actuators is presented. Three networks (ADALINE network, a nonlinear neuron network, and multilayer perceptron network) are trained and applied to an experimental hydraulic system to identify the gap between a...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Borras P, C., Stalford, H.L.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Applied sciences Computer science control theory systems Control theory. Systems Exact sciences and technology Hydraulic actuators Hydraulic systems Intelligent networks Least squares approximation Multilayer perceptrons Neural networks Neurons Pattern recognition Testing Vectors
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	405 vol.1
container_issue
container_start_page	400
container_title
container_volume	1
creator	Borras P, C. Stalford, H.L.
description	An approach for estimating and classifying backlash clearance fault condition in hydraulic actuators is presented. Three networks (ADALINE network, a nonlinear neuron network, and multilayer perceptron network) are trained and applied to an experimental hydraulic system to identify the gap between an actuator pin and a load mass. The networks are trained on five clearance gaps of widths 1, 7, 12, 25, and 40 thousandths of an inch. They are tested on three clearance gaps of widths 10, 20, and 35 thousandths of an inch. The multilayer perceptron network performed very well in all testing. The other two networks did not perform well, except for small gaps.
doi_str_mv	10.1109/ACC.2002.1024838
format	Conference Proceeding
fullrecord	<record><control><sourceid>pascalfrancis_6IE</sourceid><recordid>TN_cdi_ieee_primary_1024838</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1024838</ieee_id><sourcerecordid>15715618</sourcerecordid><originalsourceid>FETCH-LOGICAL-i203t-74bd4a1554395029cd2dbeef85d5ba7b00027061205b521879c16c32a52b46473</originalsourceid><addsrcrecordid>eNpFkEtLAzEUhYMPsK3uBTezcTn13ryzEcpQH1DQha5Lkknb2DEtyRTpv7cwgqtvcT4OnEPILcIUEczDrGmmFIBOESjXTJ-REWVK10JLPCdjUBqYokbDBRmB4qxGieaKjEv5AkBjJIzI47vt-5BTlYPfrVPs4y5VMVWbY5vtoYu-ctZvO1s21aHEtK5SOGTbndD_7PL2mlyubFfCzR8n5PNp_tG81Iu359dmtqgjBdbXiruWWxSCMyOAGt_S1oWw0qIVzioHpxUKJFIQTlDUyniUnlErqOOSKzYh90Pv3hZvu1W2ycey3Of4bfNxiUKhkKhP3t3gxRDCfzz8w34B46lV1A</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Pattern recognition in hydraulic backlash using neural network</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Borras P, C. ; Stalford, H.L.</creator><creatorcontrib>Borras P, C. ; Stalford, H.L.</creatorcontrib><description>An approach for estimating and classifying backlash clearance fault condition in hydraulic actuators is presented. Three networks (ADALINE network, a nonlinear neuron network, and multilayer perceptron network) are trained and applied to an experimental hydraulic system to identify the gap between an actuator pin and a load mass. The networks are trained on five clearance gaps of widths 1, 7, 12, 25, and 40 thousandths of an inch. They are tested on three clearance gaps of widths 10, 20, and 35 thousandths of an inch. The multilayer perceptron network performed very well in all testing. The other two networks did not perform well, except for small gaps.</description><identifier>ISSN: 0743-1619</identifier><identifier>ISBN: 0780372980</identifier><identifier>ISBN: 9780780372986</identifier><identifier>EISSN: 2378-5861</identifier><identifier>DOI: 10.1109/ACC.2002.1024838</identifier><language>eng</language><publisher>Piscataway NJ: IEEE</publisher><subject>Applied sciences ; Computer science; control theory; systems ; Control theory. Systems ; Exact sciences and technology ; Hydraulic actuators ; Hydraulic systems ; Intelligent networks ; Least squares approximation ; Multilayer perceptrons ; Neural networks ; Neurons ; Pattern recognition ; Testing ; Vectors</subject><ispartof>Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2002, Vol.1, p.400-405 vol.1</ispartof><rights>2004 INIST-CNRS</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1024838$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1024838$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15715618$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Borras P, C.</creatorcontrib><creatorcontrib>Stalford, H.L.</creatorcontrib><title>Pattern recognition in hydraulic backlash using neural network</title><title>Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)</title><addtitle>ACC</addtitle><description>An approach for estimating and classifying backlash clearance fault condition in hydraulic actuators is presented. Three networks (ADALINE network, a nonlinear neuron network, and multilayer perceptron network) are trained and applied to an experimental hydraulic system to identify the gap between an actuator pin and a load mass. The networks are trained on five clearance gaps of widths 1, 7, 12, 25, and 40 thousandths of an inch. They are tested on three clearance gaps of widths 10, 20, and 35 thousandths of an inch. The multilayer perceptron network performed very well in all testing. The other two networks did not perform well, except for small gaps.</description><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Control theory. Systems</subject><subject>Exact sciences and technology</subject><subject>Hydraulic actuators</subject><subject>Hydraulic systems</subject><subject>Intelligent networks</subject><subject>Least squares approximation</subject><subject>Multilayer perceptrons</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Pattern recognition</subject><subject>Testing</subject><subject>Vectors</subject><issn>0743-1619</issn><issn>2378-5861</issn><isbn>0780372980</isbn><isbn>9780780372986</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2002</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkEtLAzEUhYMPsK3uBTezcTn13ryzEcpQH1DQha5Lkknb2DEtyRTpv7cwgqtvcT4OnEPILcIUEczDrGmmFIBOESjXTJ-REWVK10JLPCdjUBqYokbDBRmB4qxGieaKjEv5AkBjJIzI47vt-5BTlYPfrVPs4y5VMVWbY5vtoYu-ctZvO1s21aHEtK5SOGTbndD_7PL2mlyubFfCzR8n5PNp_tG81Iu359dmtqgjBdbXiruWWxSCMyOAGt_S1oWw0qIVzioHpxUKJFIQTlDUyniUnlErqOOSKzYh90Pv3hZvu1W2ycey3Of4bfNxiUKhkKhP3t3gxRDCfzz8w34B46lV1A</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>Borras P, C.</creator><creator>Stalford, H.L.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>IQODW</scope></search><sort><creationdate>2002</creationdate><title>Pattern recognition in hydraulic backlash using neural network</title><author>Borras P, C. ; Stalford, H.L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-74bd4a1554395029cd2dbeef85d5ba7b00027061205b521879c16c32a52b46473</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Control theory. Systems</topic><topic>Exact sciences and technology</topic><topic>Hydraulic actuators</topic><topic>Hydraulic systems</topic><topic>Intelligent networks</topic><topic>Least squares approximation</topic><topic>Multilayer perceptrons</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Pattern recognition</topic><topic>Testing</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Borras P, C.</creatorcontrib><creatorcontrib>Stalford, H.L.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Borras P, C.</au><au>Stalford, H.L.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Pattern recognition in hydraulic backlash using neural network</atitle><btitle>Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)</btitle><stitle>ACC</stitle><date>2002</date><risdate>2002</risdate><volume>1</volume><spage>400</spage><epage>405 vol.1</epage><pages>400-405 vol.1</pages><issn>0743-1619</issn><eissn>2378-5861</eissn><isbn>0780372980</isbn><isbn>9780780372986</isbn><abstract>An approach for estimating and classifying backlash clearance fault condition in hydraulic actuators is presented. Three networks (ADALINE network, a nonlinear neuron network, and multilayer perceptron network) are trained and applied to an experimental hydraulic system to identify the gap between an actuator pin and a load mass. The networks are trained on five clearance gaps of widths 1, 7, 12, 25, and 40 thousandths of an inch. They are tested on three clearance gaps of widths 10, 20, and 35 thousandths of an inch. The multilayer perceptron network performed very well in all testing. The other two networks did not perform well, except for small gaps.</abstract><cop>Piscataway NJ</cop><pub>IEEE</pub><doi>10.1109/ACC.2002.1024838</doi><tpages>6</tpages></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISSN: 0743-1619
ispartof	Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2002, Vol.1, p.400-405 vol.1
issn	0743-1619 2378-5861
language	eng
recordid	cdi_ieee_primary_1024838
source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Applied sciences Computer science control theory systems Control theory. Systems Exact sciences and technology Hydraulic actuators Hydraulic systems Intelligent networks Least squares approximation Multilayer perceptrons Neural networks Neurons Pattern recognition Testing Vectors
title	Pattern recognition in hydraulic backlash using neural network
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T19%3A52%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Pattern%20recognition%20in%20hydraulic%20backlash%20using%20neural%20network&rft.btitle=Proceedings%20of%20the%202002%20American%20Control%20Conference%20(IEEE%20Cat.%20No.CH37301)&rft.au=Borras%20P,%20C.&rft.date=2002&rft.volume=1&rft.spage=400&rft.epage=405%20vol.1&rft.pages=400-405%20vol.1&rft.issn=0743-1619&rft.eissn=2378-5861&rft.isbn=0780372980&rft.isbn_list=9780780372986&rft_id=info:doi/10.1109/ACC.2002.1024838&rft_dat=%3Cpascalfrancis_6IE%3E15715618%3C/pascalfrancis_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=1024838&rfr_iscdi=true