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Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines
Abstract The predictive maintenance approach in the fault diagnosis of rotating machines is becoming important in industries in order to minimize operational cost and to improve the reliability of machines. Recent studies have focused on developing an effective prediction model to detect machinery f...
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| Published in: | Measurement science & technology 2023-12, Vol.34 (12), p.125158 |
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| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c243t-dd18174cde1b8a01f5bf13024ed233e681cba432e8822c2b98512e28bf9475223 |
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| cites | cdi_FETCH-LOGICAL-c243t-dd18174cde1b8a01f5bf13024ed233e681cba432e8822c2b98512e28bf9475223 |
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| container_issue | 12 |
| container_start_page | 125158 |
| container_title | Measurement science & technology |
| container_volume | 34 |
| creator | N, Kannan M, Saimurugan S, Sowmya Edinbarough, Immanuel |
| description | Abstract
The predictive maintenance approach in the fault diagnosis of rotating machines is becoming important in industries in order to minimize operational cost and to improve the reliability of machines. Recent studies have focused on developing an effective prediction model to detect machinery faults at various speeds using machine learning techniques. In this research study, machine-learning-based models are developed to detect multi-component faults such as gear faults, bearing faults, and unbalanced shafts, by training the four speed data sets using decision trees, support vector machines and artificial neural networks. The machine learning models are improvised by fusing the vibration signals (
X, Y
and
Z
) and sound signals with the feature selection algorithm, minimal redundancy-maximum relevance (mRMR). The research work is extended by training a model with any three speeds and testing the trained model with the remaining speed. The state-of-the-art algorithms used to train all four speeds yield poor performance in the prediction of faults at testing speeds. A linear and quadratic discriminant analysis (QDA) is chosen based on its multivariate discrimination capability for better fault prediction at testing speed. The proposed combination of QDA with mRMR selection of fused vibration and sound feature set perform well in the detection of multiple faults at variable speeds. |
| doi_str_mv | 10.1088/1361-6501/acf8e1 |
| format | article |
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The predictive maintenance approach in the fault diagnosis of rotating machines is becoming important in industries in order to minimize operational cost and to improve the reliability of machines. Recent studies have focused on developing an effective prediction model to detect machinery faults at various speeds using machine learning techniques. In this research study, machine-learning-based models are developed to detect multi-component faults such as gear faults, bearing faults, and unbalanced shafts, by training the four speed data sets using decision trees, support vector machines and artificial neural networks. The machine learning models are improvised by fusing the vibration signals (
X, Y
and
Z
) and sound signals with the feature selection algorithm, minimal redundancy-maximum relevance (mRMR). The research work is extended by training a model with any three speeds and testing the trained model with the remaining speed. The state-of-the-art algorithms used to train all four speeds yield poor performance in the prediction of faults at testing speeds. A linear and quadratic discriminant analysis (QDA) is chosen based on its multivariate discrimination capability for better fault prediction at testing speed. The proposed combination of QDA with mRMR selection of fused vibration and sound feature set perform well in the detection of multiple faults at variable speeds.</description><identifier>ISSN: 0957-0233</identifier><identifier>EISSN: 1361-6501</identifier><identifier>DOI: 10.1088/1361-6501/acf8e1</identifier><language>eng</language><ispartof>Measurement science & technology, 2023-12, Vol.34 (12), p.125158</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c243t-dd18174cde1b8a01f5bf13024ed233e681cba432e8822c2b98512e28bf9475223</citedby><cites>FETCH-LOGICAL-c243t-dd18174cde1b8a01f5bf13024ed233e681cba432e8822c2b98512e28bf9475223</cites><orcidid>0000-0002-0696-3874</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids></links><search><creatorcontrib>N, Kannan</creatorcontrib><creatorcontrib>M, Saimurugan</creatorcontrib><creatorcontrib>S, Sowmya</creatorcontrib><creatorcontrib>Edinbarough, Immanuel</creatorcontrib><title>Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines</title><title>Measurement science & technology</title><description>Abstract
The predictive maintenance approach in the fault diagnosis of rotating machines is becoming important in industries in order to minimize operational cost and to improve the reliability of machines. Recent studies have focused on developing an effective prediction model to detect machinery faults at various speeds using machine learning techniques. In this research study, machine-learning-based models are developed to detect multi-component faults such as gear faults, bearing faults, and unbalanced shafts, by training the four speed data sets using decision trees, support vector machines and artificial neural networks. The machine learning models are improvised by fusing the vibration signals (
X, Y
and
Z
) and sound signals with the feature selection algorithm, minimal redundancy-maximum relevance (mRMR). The research work is extended by training a model with any three speeds and testing the trained model with the remaining speed. The state-of-the-art algorithms used to train all four speeds yield poor performance in the prediction of faults at testing speeds. A linear and quadratic discriminant analysis (QDA) is chosen based on its multivariate discrimination capability for better fault prediction at testing speed. The proposed combination of QDA with mRMR selection of fused vibration and sound feature set perform well in the detection of multiple faults at variable speeds.</description><issn>0957-0233</issn><issn>1361-6501</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNo9kE1LAzEQhoMoWKt3j_kDazPJbjd7lFI_oOBFz8tsMmkjbbYmWaQH_7u7VLzMwMN88D6M3YN4AKH1AtQSimUlYIHGaYILNvtHl2wmmqouhFTqmt2k9CmEqEXTzNjPOuwwGLL8a0AbMXvDrU8m-oMPGDLHgPtT8ol_-7zjiULqI09-O2LuhuT7wN1EjkS28MHSkcYyLjoc9plbymTyNOUDj30eH4QtP6DZ-UDpll053Ce6--tz9vG0fl-9FJu359fV46YwslS5sBY01KWxBJ1GAa7qHCghS7JjIlpqMB2WSpLWUhrZNboCSVJ3rinrSko1Z-J818Q-pUiuPY4BMZ5aEO2kr51ctZOr9qxP_QKx5mcL</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>N, Kannan</creator><creator>M, Saimurugan</creator><creator>S, Sowmya</creator><creator>Edinbarough, Immanuel</creator><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-0696-3874</orcidid></search><sort><creationdate>20231201</creationdate><title>Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines</title><author>N, Kannan ; M, Saimurugan ; S, Sowmya ; Edinbarough, Immanuel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c243t-dd18174cde1b8a01f5bf13024ed233e681cba432e8822c2b98512e28bf9475223</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>N, Kannan</creatorcontrib><creatorcontrib>M, Saimurugan</creatorcontrib><creatorcontrib>S, Sowmya</creatorcontrib><creatorcontrib>Edinbarough, Immanuel</creatorcontrib><collection>CrossRef</collection><jtitle>Measurement science & technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>N, Kannan</au><au>M, Saimurugan</au><au>S, Sowmya</au><au>Edinbarough, Immanuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines</atitle><jtitle>Measurement science & technology</jtitle><date>2023-12-01</date><risdate>2023</risdate><volume>34</volume><issue>12</issue><spage>125158</spage><pages>125158-</pages><issn>0957-0233</issn><eissn>1361-6501</eissn><abstract>Abstract
The predictive maintenance approach in the fault diagnosis of rotating machines is becoming important in industries in order to minimize operational cost and to improve the reliability of machines. Recent studies have focused on developing an effective prediction model to detect machinery faults at various speeds using machine learning techniques. In this research study, machine-learning-based models are developed to detect multi-component faults such as gear faults, bearing faults, and unbalanced shafts, by training the four speed data sets using decision trees, support vector machines and artificial neural networks. The machine learning models are improvised by fusing the vibration signals (
X, Y
and
Z
) and sound signals with the feature selection algorithm, minimal redundancy-maximum relevance (mRMR). The research work is extended by training a model with any three speeds and testing the trained model with the remaining speed. The state-of-the-art algorithms used to train all four speeds yield poor performance in the prediction of faults at testing speeds. A linear and quadratic discriminant analysis (QDA) is chosen based on its multivariate discrimination capability for better fault prediction at testing speed. The proposed combination of QDA with mRMR selection of fused vibration and sound feature set perform well in the detection of multiple faults at variable speeds.</abstract><doi>10.1088/1361-6501/acf8e1</doi><orcidid>https://orcid.org/0000-0002-0696-3874</orcidid></addata></record> |
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| ispartof | Measurement science & technology, 2023-12, Vol.34 (12), p.125158 |
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| language | eng |
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| source | Institute of Physics |
| title | Enhanced quadratic discriminant analysis with sensor signal fusion for speed-independent fault detection in rotating machines |
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