Bearing fault diagnosis method based on SAVMD and CNN
Non-stationary and nonlinear vibrating signals in rotating mechanical equipment are always disturbed by noises from working environment and other devices. Bearing fault signals in mechanical systems are reduced which makes it difficult to extract fault characteristic, diagnose fault type. Selection...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2024-06-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.03.001 |
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| _version_ | 1841534082624782336 |
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| author | SONG ChunSheng LIANG YaRu LU NiFang DU Gang JIA Bo |
| author_facet | SONG ChunSheng LIANG YaRu LU NiFang DU Gang JIA Bo |
| author_sort | SONG ChunSheng |
| collection | DOAJ |
| description | Non-stationary and nonlinear vibrating signals in rotating mechanical equipment are always disturbed by noises from working environment and other devices. Bearing fault signals in mechanical systems are reduced which makes it difficult to extract fault characteristic, diagnose fault type. Selection of global optimal solution was solved by studying the parameter [<italic>K</italic>,<italic>α</italic>] about variational mode decomposition (VMD) and simulating physical system annealing， that was simulated annealing variational mode decomposition (SAVMD) extract characteristic value. The intrinsic mode function (IMF) components were reconstructed by weighted kurtosis evaluation index, put them into convolutional neural network (CNN) model for fault classification. Finally, the proposed method was verified by numerical simulations with the open bearing data of Case Western Reserve University. The accuracy rate is 99.28%. After introducing -6 dB white noise to simulate the noisy environment, the accuracy rate is 93.6%. These results show that this method can be used to achieve bearing fault diagnosis in complex working environment. |
| format | Article |
| id | doaj-art-642690b80716495aa9ca938f2e820a86 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-642690b80716495aa9ca938f2e820a862025-01-15T02:45:12ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692024-06-014650951763929565Bearing fault diagnosis method based on SAVMD and CNNSONG ChunShengLIANG YaRuLU NiFangDU GangJIA BoNon-stationary and nonlinear vibrating signals in rotating mechanical equipment are always disturbed by noises from working environment and other devices. Bearing fault signals in mechanical systems are reduced which makes it difficult to extract fault characteristic, diagnose fault type. Selection of global optimal solution was solved by studying the parameter [<italic>K</italic>,<italic>α</italic>] about variational mode decomposition (VMD) and simulating physical system annealing， that was simulated annealing variational mode decomposition (SAVMD) extract characteristic value. The intrinsic mode function (IMF) components were reconstructed by weighted kurtosis evaluation index, put them into convolutional neural network (CNN) model for fault classification. Finally, the proposed method was verified by numerical simulations with the open bearing data of Case Western Reserve University. The accuracy rate is 99.28%. After introducing -6 dB white noise to simulate the noisy environment, the accuracy rate is 93.6%. These results show that this method can be used to achieve bearing fault diagnosis in complex working environment.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.03.001Bearing faultVariational mode decompositionSimulated annealingFeature extractionFault diagnosis |
| spellingShingle | SONG ChunSheng LIANG YaRu LU NiFang DU Gang JIA Bo Bearing fault diagnosis method based on SAVMD and CNN Jixie qiangdu Bearing fault Variational mode decomposition Simulated annealing Feature extraction Fault diagnosis |
| title | Bearing fault diagnosis method based on SAVMD and CNN |
| title_full | Bearing fault diagnosis method based on SAVMD and CNN |
| title_fullStr | Bearing fault diagnosis method based on SAVMD and CNN |
| title_full_unstemmed | Bearing fault diagnosis method based on SAVMD and CNN |
| title_short | Bearing fault diagnosis method based on SAVMD and CNN |
| title_sort | bearing fault diagnosis method based on savmd and cnn |
| topic | Bearing fault Variational mode decomposition Simulated annealing Feature extraction Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2024.03.001 |
| work_keys_str_mv | AT songchunsheng bearingfaultdiagnosismethodbasedonsavmdandcnn AT liangyaru bearingfaultdiagnosismethodbasedonsavmdandcnn AT lunifang bearingfaultdiagnosismethodbasedonsavmdandcnn AT dugang bearingfaultdiagnosismethodbasedonsavmdandcnn AT jiabo bearingfaultdiagnosismethodbasedonsavmdandcnn |