Motor Fault Diagnosis Based on Scale Invariant Image Features
Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship betw...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2022-03, Vol.18 (3), p.1605-1617 |
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| container_title | IEEE transactions on industrial informatics |
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| creator | Long, Zhuo Zhang, Xiaofei He, Min Huang, Shoudao Qin, Guojun Song, Dianyi Tang, Yao Wu, Gongping Liang, Weizhi Shao, Haidong |
| description | Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence. |
| doi_str_mv | 10.1109/TII.2021.3084615 |
| format | Article |
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This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence.</description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2021.3084615</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Data mining ; Dictionaries ; Fault detection ; Fault diagnosis ; Feature extraction ; Image processing ; Industrial applications ; Invariants ; Machine learning ; Motor fault diagnosis ; scale-invariant feature transform (SIFT) ; Symmetrized dot pattern ; symmetrized dot pattern (SDP) ; Time-frequency analysis ; Transforms ; visual knowledge ; Visualization ; Working conditions</subject><ispartof>IEEE transactions on industrial informatics, 2022-03, Vol.18 (3), p.1605-1617</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-f733ee708bf8f1096862360fdecbdb6907612c7f67078aab7988a3e2797811213</citedby><cites>FETCH-LOGICAL-c291t-f733ee708bf8f1096862360fdecbdb6907612c7f67078aab7988a3e2797811213</cites><orcidid>0000-0002-6923-9605 ; 0000-0001-7106-0009 ; 0000-0002-8256-0401 ; 0000-0003-1316-0153 ; 0000-0002-8855-781X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9444210$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9444210$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Long, Zhuo</creatorcontrib><creatorcontrib>Zhang, Xiaofei</creatorcontrib><creatorcontrib>He, Min</creatorcontrib><creatorcontrib>Huang, Shoudao</creatorcontrib><creatorcontrib>Qin, Guojun</creatorcontrib><creatorcontrib>Song, Dianyi</creatorcontrib><creatorcontrib>Tang, Yao</creatorcontrib><creatorcontrib>Wu, Gongping</creatorcontrib><creatorcontrib>Liang, Weizhi</creatorcontrib><creatorcontrib>Shao, Haidong</creatorcontrib><title>Motor Fault Diagnosis Based on Scale Invariant Image Features</title><title>IEEE transactions on industrial informatics</title><addtitle>TII</addtitle><description>Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence.</description><subject>Algorithms</subject><subject>Data mining</subject><subject>Dictionaries</subject><subject>Fault detection</subject><subject>Fault diagnosis</subject><subject>Feature extraction</subject><subject>Image processing</subject><subject>Industrial applications</subject><subject>Invariants</subject><subject>Machine learning</subject><subject>Motor fault diagnosis</subject><subject>scale-invariant feature transform (SIFT)</subject><subject>Symmetrized dot pattern</subject><subject>symmetrized dot pattern (SDP)</subject><subject>Time-frequency analysis</subject><subject>Transforms</subject><subject>visual knowledge</subject><subject>Visualization</subject><subject>Working conditions</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1Lw0AQhhdRsFbvgpcFz6kzu8l-HDxoazVQ8WA9L5t0UlLapO4mgv_elBZPM4f3eWd4GLtFmCCCfVjm-USAwIkEkyrMztgIbYoJQAbnw55lmEgB8pJdxbgBkBqkHbHH97ZrA5_7ftvxWe3XTRvryJ99pBVvG_5Z-i3xvPnxofZNx_OdXxOfk-_6QPGaXVR-G-nmNMfsa_6ynL4li4_XfPq0SEphsUsqLSWRBlNUphqeVUYJqaBaUVmsCmVBKxSlrpQGbbwvtDXGSxLaaoMoUI7Z_bF3H9rvnmLnNm0fmuGkE2pgDGbSDCk4psrQxhiocvtQ73z4dQjuIMkNktxBkjtJGpC7I1IT0X_cpmkqEOQfF01gPw</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Long, Zhuo</creator><creator>Zhang, Xiaofei</creator><creator>He, Min</creator><creator>Huang, Shoudao</creator><creator>Qin, Guojun</creator><creator>Song, Dianyi</creator><creator>Tang, Yao</creator><creator>Wu, Gongping</creator><creator>Liang, Weizhi</creator><creator>Shao, Haidong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. 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| subjects | Algorithms Data mining Dictionaries Fault detection Fault diagnosis Feature extraction Image processing Industrial applications Invariants Machine learning Motor fault diagnosis scale-invariant feature transform (SIFT) Symmetrized dot pattern symmetrized dot pattern (SDP) Time-frequency analysis Transforms visual knowledge Visualization Working conditions |
| title | Motor Fault Diagnosis Based on Scale Invariant Image Features |
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