RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN
Aiming at the high dependence of super parameter selection on artificial experience in rolling bearing state identification based on convolution neural network(CNN),a fault diagnosis model(CNN⁃MA)based on mayfly algorithm(MA)was proposed.Firstly,the model used the powerful optimization ability of MA...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-08-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.005 |
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| _version_ | 1841534137888931840 |
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| author | TIAN LiYong ZHAO JianJun YU Ning |
| author_facet | TIAN LiYong ZHAO JianJun YU Ning |
| author_sort | TIAN LiYong |
| collection | DOAJ |
| description | Aiming at the high dependence of super parameter selection on artificial experience in rolling bearing state identification based on convolution neural network(CNN),a fault diagnosis model(CNN⁃MA)based on mayfly algorithm(MA)was proposed.Firstly,the model used the powerful optimization ability of MA,took the diagnostic accuracy of CNN as the optimization objective,and adaptively adjusted the super parameters in CNN.Secondly,the normalized original signal image set was used to preserve the characteristics of the signal as much as possible.Finally,in order to evaluate the effectiveness of the parameters in the optimization model,compared with the CNN model optimized by particle swarm optimization(PSO)algorithm.The results show that the proposed model has more stable performance,higher recognition accuracy and good anti⁃noise ability.It fully shows the feasibility and reliability of CNN⁃MA model in fault diagnosis of rolling bearings. |
| format | Article |
| id | doaj-art-febf0ae3f4704103b0da966836bc83d2 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-08-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-febf0ae3f4704103b0da966836bc83d22025-01-15T02:45:54ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-08-014679580179314236RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNNTIAN LiYongZHAO JianJunYU NingAiming at the high dependence of super parameter selection on artificial experience in rolling bearing state identification based on convolution neural network(CNN),a fault diagnosis model(CNN⁃MA)based on mayfly algorithm(MA)was proposed.Firstly,the model used the powerful optimization ability of MA,took the diagnostic accuracy of CNN as the optimization objective,and adaptively adjusted the super parameters in CNN.Secondly,the normalized original signal image set was used to preserve the characteristics of the signal as much as possible.Finally,in order to evaluate the effectiveness of the parameters in the optimization model,compared with the CNN model optimized by particle swarm optimization(PSO)algorithm.The results show that the proposed model has more stable performance,higher recognition accuracy and good anti⁃noise ability.It fully shows the feasibility and reliability of CNN⁃MA model in fault diagnosis of rolling bearings.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.005Convolution neural networkMayfly algorithmRolling bearingFault diagnosis |
| spellingShingle | TIAN LiYong ZHAO JianJun YU Ning RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN Jixie qiangdu Convolution neural network Mayfly algorithm Rolling bearing Fault diagnosis |
| title | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN |
| title_full | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN |
| title_fullStr | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN |
| title_full_unstemmed | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN |
| title_short | RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON MA OPTIMIZATION OF CNN |
| title_sort | research on rolling bearing fault diagnosis based on ma optimization of cnn |
| topic | Convolution neural network Mayfly algorithm Rolling bearing Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.04.005 |
| work_keys_str_mv | AT tianliyong researchonrollingbearingfaultdiagnosisbasedonmaoptimizationofcnn AT zhaojianjun researchonrollingbearingfaultdiagnosisbasedonmaoptimizationofcnn AT yuning researchonrollingbearingfaultdiagnosisbasedonmaoptimizationofcnn |