TWO-STAGE INTELLIGENT FAULT DIAGNOSIS OF MOTORS BASED ON VISUAL IMAGE FEATURES
Aiming at the new challenges in efficiency and reliability in the field of fault diagnosis in recent years,a coarsefine fault diagnosis method for induction motors based on the symmetrized dot pattern(SDP)was proposed.In this method,firstly,the vibration signals of each faulty motor were converted i...
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| Format: | Article |
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| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2024-01-01
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| Series: | Jixie qiangdu |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.06.004 |
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| collection | DOAJ |
| description | Aiming at the new challenges in efficiency and reliability in the field of fault diagnosis in recent years,a coarsefine fault diagnosis method for induction motors based on the symmetrized dot pattern(SDP)was proposed.In this method,firstly,the vibration signals of each faulty motor were converted into snowflake images by SDP method,and then a two-stage fault diagnosis framework of coarse-fine classification was designed for image feature extraction and classification.In the coarse classification stage,the color histogram features and the support vector machine(SVM)were used to diagnose the samples,and a threshold was selected to determine the samples for the coarse classification.In the fine classification stage,Gist features that can extract image details and SVM were used to diagnose the remaining samples.Experimental results show that the proposed method combines the advantages of color histogram features and Gist features,can achieve the most reliable diagnosis with relatively high efficiency,and has certain anti-noise ability. |
| format | Article |
| id | doaj-art-94eef60c169345fe85c008a57ffd3941 |
| institution | OA Journals |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-94eef60c169345fe85c008a57ffd39412025-08-20T01:47:28ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-01-01461295130198127137TWO-STAGE INTELLIGENT FAULT DIAGNOSIS OF MOTORS BASED ON VISUAL IMAGE FEATURESAiming at the new challenges in efficiency and reliability in the field of fault diagnosis in recent years,a coarsefine fault diagnosis method for induction motors based on the symmetrized dot pattern(SDP)was proposed.In this method,firstly,the vibration signals of each faulty motor were converted into snowflake images by SDP method,and then a two-stage fault diagnosis framework of coarse-fine classification was designed for image feature extraction and classification.In the coarse classification stage,the color histogram features and the support vector machine(SVM)were used to diagnose the samples,and a threshold was selected to determine the samples for the coarse classification.In the fine classification stage,Gist features that can extract image details and SVM were used to diagnose the remaining samples.Experimental results show that the proposed method combines the advantages of color histogram features and Gist features,can achieve the most reliable diagnosis with relatively high efficiency,and has certain anti-noise ability.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.06.004 |
| spellingShingle | TWO-STAGE INTELLIGENT FAULT DIAGNOSIS OF MOTORS BASED ON VISUAL IMAGE FEATURES Jixie qiangdu |
| title | TWO-STAGE INTELLIGENT FAULT DIAGNOSIS OF MOTORS BASED ON VISUAL IMAGE FEATURES |
| title_full | TWO-STAGE INTELLIGENT FAULT DIAGNOSIS OF MOTORS BASED ON VISUAL IMAGE FEATURES |
| title_fullStr | TWO-STAGE INTELLIGENT FAULT DIAGNOSIS OF MOTORS BASED ON VISUAL IMAGE FEATURES |
| title_full_unstemmed | TWO-STAGE INTELLIGENT FAULT DIAGNOSIS OF MOTORS BASED ON VISUAL IMAGE FEATURES |
| title_short | TWO-STAGE INTELLIGENT FAULT DIAGNOSIS OF MOTORS BASED ON VISUAL IMAGE FEATURES |
| title_sort | two stage intelligent fault diagnosis of motors based on visual image features |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.06.004 |