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Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers
This paper presents a novel association rule mining (ARM)-based dissolved gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for cho...
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| Published in: | IEEE transactions on human-machine systems 2009-11, Vol.39 (6), p.597-610 |
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| cites | cdi_FETCH-LOGICAL-c356t-cc8316f37ebee2f061b667890dde109925e25e29e7832b2892538b19f223bab33 |
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| container_title | IEEE transactions on human-machine systems |
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| creator | Yang, Z. Tang, W.H. Shintemirov, A. Wu, Q.H. |
| description | This paper presents a novel association rule mining (ARM)-based dissolved gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for choosing user-interested ARM attributes from a DGA data set, i.e. the items that are employed to extract association rules. The given DGA data set is composed of two parts, i.e. training and test DGA data sets. An ARM algorithm namely Apriori-Total From Partial is proposed for generating an association rule set (ARS) from the training DGA data set. Afterwards, an ARS simplification method and a rule fitness evaluation method are utilized to select useful rules from the ARS and assign a fitness value to each of the useful rules, respectively. Based upon the useful association rules, a transformer FD classifier is developed, in which an optimal rule selection method is employed for selecting the most accurate rule from the classifier for diagnosing a test DGA record. For comparison purposes, five widely used FD methods are also tested with the same training and test data sets in experiments. Results show that the proposed ARM-based DGA approach is capable of generating a number of meaningful association rules, which can also cover the empirical rules defined in industry standards. Moreover, a higher FD accuracy can be achieved with the association rule-based FD classifier, compared with that derived by the other methods. |
| doi_str_mv | 10.1109/TSMCC.2009.2021989 |
| format | article |
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In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for choosing user-interested ARM attributes from a DGA data set, i.e. the items that are employed to extract association rules. The given DGA data set is composed of two parts, i.e. training and test DGA data sets. An ARM algorithm namely Apriori-Total From Partial is proposed for generating an association rule set (ARS) from the training DGA data set. Afterwards, an ARS simplification method and a rule fitness evaluation method are utilized to select useful rules from the ARS and assign a fitness value to each of the useful rules, respectively. Based upon the useful association rules, a transformer FD classifier is developed, in which an optimal rule selection method is employed for selecting the most accurate rule from the classifier for diagnosing a test DGA record. For comparison purposes, five widely used FD methods are also tested with the same training and test data sets in experiments. Results show that the proposed ARM-based DGA approach is capable of generating a number of meaningful association rules, which can also cover the empirical rules defined in industry standards. Moreover, a higher FD accuracy can be achieved with the association rule-based FD classifier, compared with that derived by the other methods.</description><identifier>ISSN: 1094-6977</identifier><identifier>ISSN: 2168-2291</identifier><identifier>EISSN: 1558-2442</identifier><identifier>EISSN: 2168-2305</identifier><identifier>DOI: 10.1109/TSMCC.2009.2021989</identifier><identifier>CODEN: ITCRFH</identifier><language>eng</language><publisher>New-York, NY: IEEE</publisher><subject>Applied sciences ; Apriori-TFP ; association rule mining ; Association rules ; Bootstrap ; Classifiers ; Computer science; control theory; systems ; Data mining ; Data processing. List processing. Character string processing ; Dissolution ; Dissolved gas analysis ; Electrical engineering. Electrical power engineering ; Electrical fault detection ; Exact sciences and technology ; Fault diagnosis ; Fitness ; Gas analysis ; Industrial metrology. Testing ; Mechanical engineering. Machine design ; Memory organisation. Data processing ; Oil insulation ; Power system reliability ; power transformer ; Power transformer insulation ; Power transformers ; Software ; Studies ; Testing ; Training ; Transformers ; Transformers and inductors</subject><ispartof>IEEE transactions on human-machine systems, 2009-11, Vol.39 (6), p.597-610</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-cc8316f37ebee2f061b667890dde109925e25e29e7832b2892538b19f223bab33</citedby><cites>FETCH-LOGICAL-c356t-cc8316f37ebee2f061b667890dde109925e25e29e7832b2892538b19f223bab33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5164914$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,783,787,27936,27937,55124</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22159412$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Z.</creatorcontrib><creatorcontrib>Tang, W.H.</creatorcontrib><creatorcontrib>Shintemirov, A.</creatorcontrib><creatorcontrib>Wu, Q.H.</creatorcontrib><title>Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers</title><title>IEEE transactions on human-machine systems</title><addtitle>TSMCC</addtitle><description>This paper presents a novel association rule mining (ARM)-based dissolved gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for choosing user-interested ARM attributes from a DGA data set, i.e. the items that are employed to extract association rules. The given DGA data set is composed of two parts, i.e. training and test DGA data sets. An ARM algorithm namely Apriori-Total From Partial is proposed for generating an association rule set (ARS) from the training DGA data set. Afterwards, an ARS simplification method and a rule fitness evaluation method are utilized to select useful rules from the ARS and assign a fitness value to each of the useful rules, respectively. Based upon the useful association rules, a transformer FD classifier is developed, in which an optimal rule selection method is employed for selecting the most accurate rule from the classifier for diagnosing a test DGA record. For comparison purposes, five widely used FD methods are also tested with the same training and test data sets in experiments. Results show that the proposed ARM-based DGA approach is capable of generating a number of meaningful association rules, which can also cover the empirical rules defined in industry standards. Moreover, a higher FD accuracy can be achieved with the association rule-based FD classifier, compared with that derived by the other methods.</description><subject>Applied sciences</subject><subject>Apriori-TFP</subject><subject>association rule mining</subject><subject>Association rules</subject><subject>Bootstrap</subject><subject>Classifiers</subject><subject>Computer science; control theory; systems</subject><subject>Data mining</subject><subject>Data processing. List processing. Character string processing</subject><subject>Dissolution</subject><subject>Dissolved gas analysis</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Electrical fault detection</subject><subject>Exact sciences and technology</subject><subject>Fault diagnosis</subject><subject>Fitness</subject><subject>Gas analysis</subject><subject>Industrial metrology. Testing</subject><subject>Mechanical engineering. Machine design</subject><subject>Memory organisation. Data processing</subject><subject>Oil insulation</subject><subject>Power system reliability</subject><subject>power transformer</subject><subject>Power transformer insulation</subject><subject>Power transformers</subject><subject>Software</subject><subject>Studies</subject><subject>Testing</subject><subject>Training</subject><subject>Transformers</subject><subject>Transformers and inductors</subject><issn>1094-6977</issn><issn>2168-2291</issn><issn>1558-2442</issn><issn>2168-2305</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNpdkN9LwzAQgIsoOKf_gL4UQXzqzK82yeOcbgobis5XS9pdR0bXzNyq7L83c2MPQkiOu--OyxdFl5T0KCX6bvo-GQx6jBAdLka10kdRh6apSpgQ7DjERIsk01KeRmeIC0KoEJp3os8-oiutWVvXxG9tDfHENraZJ_cGYRY_2FCuv0M0Mhj3G1Nv0GJcOR8PTVuvA2DmjdvmXBW_uh_w8dSbBgOxBI_n0UllaoSL_duNPoaP08FTMn4ZPQ_646TkabZOylJxmlVcQgHAKpLRIsuk0mQ2g7C5ZilsjwapOCuYCgmuCqorxnhhCs670e1u7sq7rxZwnS8tllDXpgHXYq5kSrjUkgTy-h-5cK0PHwtQqoQkwWGA2A4qvUP0UOUrb5fGb3JK8q3w_E94vhWe74WHppv9ZIOlqaugobR46GSMplpQFrirHWcB4FBOaSY0FfwXT4yIig</recordid><startdate>20091101</startdate><enddate>20091101</enddate><creator>Yang, Z.</creator><creator>Tang, W.H.</creator><creator>Shintemirov, A.</creator><creator>Wu, Q.H.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><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><scope>F28</scope></search><sort><creationdate>20091101</creationdate><title>Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers</title><author>Yang, Z. ; Tang, W.H. ; Shintemirov, A. ; Wu, Q.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-cc8316f37ebee2f061b667890dde109925e25e29e7832b2892538b19f223bab33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Applied sciences</topic><topic>Apriori-TFP</topic><topic>association rule mining</topic><topic>Association rules</topic><topic>Bootstrap</topic><topic>Classifiers</topic><topic>Computer science; control theory; systems</topic><topic>Data mining</topic><topic>Data processing. List processing. Character string processing</topic><topic>Dissolution</topic><topic>Dissolved gas analysis</topic><topic>Electrical engineering. Electrical power engineering</topic><topic>Electrical fault detection</topic><topic>Exact sciences and technology</topic><topic>Fault diagnosis</topic><topic>Fitness</topic><topic>Gas analysis</topic><topic>Industrial metrology. Testing</topic><topic>Mechanical engineering. Machine design</topic><topic>Memory organisation. Data processing</topic><topic>Oil insulation</topic><topic>Power system reliability</topic><topic>power transformer</topic><topic>Power transformer insulation</topic><topic>Power transformers</topic><topic>Software</topic><topic>Studies</topic><topic>Testing</topic><topic>Training</topic><topic>Transformers</topic><topic>Transformers and inductors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yang, Z.</creatorcontrib><creatorcontrib>Tang, W.H.</creatorcontrib><creatorcontrib>Shintemirov, A.</creatorcontrib><creatorcontrib>Wu, Q.H.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library Online</collection><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><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on human-machine systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yang, Z.</au><au>Tang, W.H.</au><au>Shintemirov, A.</au><au>Wu, Q.H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers</atitle><jtitle>IEEE transactions on human-machine systems</jtitle><stitle>TSMCC</stitle><date>2009-11-01</date><risdate>2009</risdate><volume>39</volume><issue>6</issue><spage>597</spage><epage>610</epage><pages>597-610</pages><issn>1094-6977</issn><issn>2168-2291</issn><eissn>1558-2442</eissn><eissn>2168-2305</eissn><coden>ITCRFH</coden><abstract>This paper presents a novel association rule mining (ARM)-based dissolved gas analysis (DGA) approach to fault diagnosis (FD) of power transformers. In the development of the ARM-based DGA approach, an attribute selection method and a continuous datum attribute discretization method are used for choosing user-interested ARM attributes from a DGA data set, i.e. the items that are employed to extract association rules. The given DGA data set is composed of two parts, i.e. training and test DGA data sets. An ARM algorithm namely Apriori-Total From Partial is proposed for generating an association rule set (ARS) from the training DGA data set. Afterwards, an ARS simplification method and a rule fitness evaluation method are utilized to select useful rules from the ARS and assign a fitness value to each of the useful rules, respectively. Based upon the useful association rules, a transformer FD classifier is developed, in which an optimal rule selection method is employed for selecting the most accurate rule from the classifier for diagnosing a test DGA record. For comparison purposes, five widely used FD methods are also tested with the same training and test data sets in experiments. Results show that the proposed ARM-based DGA approach is capable of generating a number of meaningful association rules, which can also cover the empirical rules defined in industry standards. Moreover, a higher FD accuracy can be achieved with the association rule-based FD classifier, compared with that derived by the other methods.</abstract><cop>New-York, NY</cop><pub>IEEE</pub><doi>10.1109/TSMCC.2009.2021989</doi><tpages>14</tpages></addata></record> |
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| identifier | ISSN: 1094-6977 |
| ispartof | IEEE transactions on human-machine systems, 2009-11, Vol.39 (6), p.597-610 |
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| subjects | Applied sciences Apriori-TFP association rule mining Association rules Bootstrap Classifiers Computer science control theory systems Data mining Data processing. List processing. Character string processing Dissolution Dissolved gas analysis Electrical engineering. Electrical power engineering Electrical fault detection Exact sciences and technology Fault diagnosis Fitness Gas analysis Industrial metrology. Testing Mechanical engineering. Machine design Memory organisation. Data processing Oil insulation Power system reliability power transformer Power transformer insulation Power transformers Software Studies Testing Training Transformers Transformers and inductors |
| title | Association Rule Mining-Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers |
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