Monitoring of induction machines by maximum covariance method for frequency tracking
Motor current signature analysis (MCSA) has been widely investigated in order to monitor fault conditions of induction machines. On the other hand several solutions were proposed for the detection of rotor speed of induction motor for sensorless control. Another deeply investigated field of research...
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creator | Bellini, A. Franceschini, G. Tassoni, C. |
description | Motor current signature analysis (MCSA) has been widely investigated in order to monitor fault conditions of induction machines. On the other hand several solutions were proposed for the detection of rotor speed of induction motor for sensorless control. Another deeply investigated field of research is the detection of supply frequency of power lines, for the diagnosis of the distribution network. A common root of these three key topics is the need of accurately stating specific spectrum frequencies. Several techniques were presented in the literature in order to perform accurate tracking of frequencies for different purposes. They are modified versions of the traditional discrete Fourier transformation (DFT), or novel spectrum estimation techniques. This paper presents a novel procedure based on the statistical analysis of the current signal in the time domain, referred to as maximum covariance method for frequency tracking (MCMFT), that allows to obtain high frequency resolution accuracy independently of the sampling frequency and of the time acquisition period. Therefore those spectrum lines related to supply frequency or to slip can be detected with extreme accuracy within a wide range of sampled data conditions. Then either an accurate diagnosis of the machine electric faults or sensorless control, or distribution network diagnosis can be performed. Comparison between the proposed method and the literature are reported, in order to critically analyze its performances. An induction machine with two artificially broken bars was used for the experiments. |
doi_str_mv | 10.1109/IAS.2004.1348497 |
format | Conference Proceeding |
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On the other hand several solutions were proposed for the detection of rotor speed of induction motor for sensorless control. Another deeply investigated field of research is the detection of supply frequency of power lines, for the diagnosis of the distribution network. A common root of these three key topics is the need of accurately stating specific spectrum frequencies. Several techniques were presented in the literature in order to perform accurate tracking of frequencies for different purposes. They are modified versions of the traditional discrete Fourier transformation (DFT), or novel spectrum estimation techniques. This paper presents a novel procedure based on the statistical analysis of the current signal in the time domain, referred to as maximum covariance method for frequency tracking (MCMFT), that allows to obtain high frequency resolution accuracy independently of the sampling frequency and of the time acquisition period. Therefore those spectrum lines related to supply frequency or to slip can be detected with extreme accuracy within a wide range of sampled data conditions. Then either an accurate diagnosis of the machine electric faults or sensorless control, or distribution network diagnosis can be performed. Comparison between the proposed method and the literature are reported, in order to critically analyze its performances. An induction machine with two artificially broken bars was used for the experiments.</description><identifier>ISSN: 0197-2618</identifier><identifier>ISBN: 0780384865</identifier><identifier>ISBN: 9780780384866</identifier><identifier>EISSN: 2576-702X</identifier><identifier>DOI: 10.1109/IAS.2004.1348497</identifier><language>eng</language><publisher>Piscataway NJ: IEEE</publisher><subject>A.c. Machines ; Applied sciences ; Condition monitoring ; Discrete Fourier transforms ; Electrical engineering. Electrical power engineering ; Electrical machines ; Exact sciences and technology ; Fault diagnosis ; Frequency ; Induction machines ; Induction motors ; Miscellaneous ; Power electronics, power supplies ; Power supplies ; Regulation and control ; Rotors ; Sensorless control ; Spectral analysis</subject><ispartof>Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting, 2004, Vol.2, p.743-749 vol.2</ispartof><rights>2006 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/1348497$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1348497$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17480689$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Bellini, A.</creatorcontrib><creatorcontrib>Franceschini, G.</creatorcontrib><creatorcontrib>Tassoni, C.</creatorcontrib><title>Monitoring of induction machines by maximum covariance method for frequency tracking</title><title>Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting</title><addtitle>IAS</addtitle><description>Motor current signature analysis (MCSA) has been widely investigated in order to monitor fault conditions of induction machines. On the other hand several solutions were proposed for the detection of rotor speed of induction motor for sensorless control. Another deeply investigated field of research is the detection of supply frequency of power lines, for the diagnosis of the distribution network. A common root of these three key topics is the need of accurately stating specific spectrum frequencies. Several techniques were presented in the literature in order to perform accurate tracking of frequencies for different purposes. They are modified versions of the traditional discrete Fourier transformation (DFT), or novel spectrum estimation techniques. This paper presents a novel procedure based on the statistical analysis of the current signal in the time domain, referred to as maximum covariance method for frequency tracking (MCMFT), that allows to obtain high frequency resolution accuracy independently of the sampling frequency and of the time acquisition period. Therefore those spectrum lines related to supply frequency or to slip can be detected with extreme accuracy within a wide range of sampled data conditions. Then either an accurate diagnosis of the machine electric faults or sensorless control, or distribution network diagnosis can be performed. Comparison between the proposed method and the literature are reported, in order to critically analyze its performances. An induction machine with two artificially broken bars was used for the experiments.</description><subject>A.c. Machines</subject><subject>Applied sciences</subject><subject>Condition monitoring</subject><subject>Discrete Fourier transforms</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Electrical machines</subject><subject>Exact sciences and technology</subject><subject>Fault diagnosis</subject><subject>Frequency</subject><subject>Induction machines</subject><subject>Induction motors</subject><subject>Miscellaneous</subject><subject>Power electronics, power supplies</subject><subject>Power supplies</subject><subject>Regulation and control</subject><subject>Rotors</subject><subject>Sensorless control</subject><subject>Spectral analysis</subject><issn>0197-2618</issn><issn>2576-702X</issn><isbn>0780384865</isbn><isbn>9780780384866</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFUEtLAzEYDD7AWnsXvOTiceuXd3IsxUdB8WAFbyWbTWy0m9TsVuy_d6GCc5mBGYZhELokMCUEzM1i9jKlAHxKGNfcqCM0okLJSgF9O0bnoDQwzbUUJ2gExKiKSqLP0KTrPmAAF5xIOULLp5xin0tM7zgHHFOzc33MCbfWrWPyHa73g_6J7a7FLn_bEm1yHre-X-cGh1xwKP5r55Pb475Y9zk0XaDTYDedn_zxGL3e3S7nD9Xj8_1iPnusIqHQV7UVtWfALVeKOmEkU5yEUFujjeNEc_DahboRhBIDLtBGOiGZU8IKzzRjY3R96N3aztlNKMO02K22Jba27FdEcQ1SmyF3dchF7_2_fTiO_QKXbF_6</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Bellini, A.</creator><creator>Franceschini, G.</creator><creator>Tassoni, C.</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>2004</creationdate><title>Monitoring of induction machines by maximum covariance method for frequency tracking</title><author>Bellini, A. ; Franceschini, G. ; Tassoni, C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i120t-ba5be304a4772c5963741ffba989c41840e8cfbd512190cf2d6c563c75a5e3833</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>A.c. Machines</topic><topic>Applied sciences</topic><topic>Condition monitoring</topic><topic>Discrete Fourier transforms</topic><topic>Electrical engineering. Electrical power engineering</topic><topic>Electrical machines</topic><topic>Exact sciences and technology</topic><topic>Fault diagnosis</topic><topic>Frequency</topic><topic>Induction machines</topic><topic>Induction motors</topic><topic>Miscellaneous</topic><topic>Power electronics, power supplies</topic><topic>Power supplies</topic><topic>Regulation and control</topic><topic>Rotors</topic><topic>Sensorless control</topic><topic>Spectral analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Bellini, A.</creatorcontrib><creatorcontrib>Franceschini, G.</creatorcontrib><creatorcontrib>Tassoni, C.</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 Online</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>Bellini, A.</au><au>Franceschini, G.</au><au>Tassoni, C.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Monitoring of induction machines by maximum covariance method for frequency tracking</atitle><btitle>Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting</btitle><stitle>IAS</stitle><date>2004</date><risdate>2004</risdate><volume>2</volume><spage>743</spage><epage>749 vol.2</epage><pages>743-749 vol.2</pages><issn>0197-2618</issn><eissn>2576-702X</eissn><isbn>0780384865</isbn><isbn>9780780384866</isbn><abstract>Motor current signature analysis (MCSA) has been widely investigated in order to monitor fault conditions of induction machines. On the other hand several solutions were proposed for the detection of rotor speed of induction motor for sensorless control. Another deeply investigated field of research is the detection of supply frequency of power lines, for the diagnosis of the distribution network. A common root of these three key topics is the need of accurately stating specific spectrum frequencies. Several techniques were presented in the literature in order to perform accurate tracking of frequencies for different purposes. They are modified versions of the traditional discrete Fourier transformation (DFT), or novel spectrum estimation techniques. This paper presents a novel procedure based on the statistical analysis of the current signal in the time domain, referred to as maximum covariance method for frequency tracking (MCMFT), that allows to obtain high frequency resolution accuracy independently of the sampling frequency and of the time acquisition period. Therefore those spectrum lines related to supply frequency or to slip can be detected with extreme accuracy within a wide range of sampled data conditions. Then either an accurate diagnosis of the machine electric faults or sensorless control, or distribution network diagnosis can be performed. Comparison between the proposed method and the literature are reported, in order to critically analyze its performances. An induction machine with two artificially broken bars was used for the experiments.</abstract><cop>Piscataway NJ</cop><pub>IEEE</pub><doi>10.1109/IAS.2004.1348497</doi><tpages>7</tpages></addata></record> |
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identifier | ISSN: 0197-2618 |
ispartof | Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting, 2004, Vol.2, p.743-749 vol.2 |
issn | 0197-2618 2576-702X |
language | eng |
recordid | cdi_pascalfrancis_primary_17480689 |
source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | A.c. Machines Applied sciences Condition monitoring Discrete Fourier transforms Electrical engineering. Electrical power engineering Electrical machines Exact sciences and technology Fault diagnosis Frequency Induction machines Induction motors Miscellaneous Power electronics, power supplies Power supplies Regulation and control Rotors Sensorless control Spectral analysis |
title | Monitoring of induction machines by maximum covariance method for frequency tracking |
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