Bearing Fault Diagnosis Based on Multilayer Domain Adaptation
Bearing fault diagnosis plays a vitally important role in practical industrial scenarios. Deep learning-based fault diagnosis methods are usually performed on the hypothesis that the training set and test set obey the same probability distribution, which is hard to satisfy under the actual working c...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/8873960 |
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| _version_ | 1832553278991237120 |
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| author | Bingru Yang Qi Li Liang Chen Changqing Shen |
| author_facet | Bingru Yang Qi Li Liang Chen Changqing Shen |
| author_sort | Bingru Yang |
| collection | DOAJ |
| description | Bearing fault diagnosis plays a vitally important role in practical industrial scenarios. Deep learning-based fault diagnosis methods are usually performed on the hypothesis that the training set and test set obey the same probability distribution, which is hard to satisfy under the actual working conditions. This paper proposes a novel multilayer domain adaptation (MLDA) method, which can diagnose the compound fault and single fault of multiple sizes simultaneously. A special designed residual network for the fault diagnosis task is pretrained to extract domain-invariant features. The multikernel maximum mean discrepancy (MK-MMD) and pseudo-label learning are adopted in multiple layers to take both marginal distributions and conditional distributions into consideration. A total of 12 transfer tasks in the fault diagnosis problem are conducted to verify the performance of MLDA. Through the comparisons of different signal processing methods, different parameter settings, and different models, it is proved that the proposed MLDA model can effectively extract domain-invariant features and achieve satisfying results. |
| format | Article |
| id | doaj-art-a112d578fea84c40b1adad6d11471f2b |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-a112d578fea84c40b1adad6d11471f2b2025-02-03T05:54:25ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/88739608873960Bearing Fault Diagnosis Based on Multilayer Domain AdaptationBingru Yang0Qi Li1Liang Chen2Changqing Shen3School of Mechanical and Electric Engineering, Soochow University, Suzhou 215131, ChinaSchool of Mechanical and Electric Engineering, Soochow University, Suzhou 215131, ChinaSchool of Mechanical and Electric Engineering, Soochow University, Suzhou 215131, ChinaSchool of Rail Transportation, Soochow University, Suzhou 215131, ChinaBearing fault diagnosis plays a vitally important role in practical industrial scenarios. Deep learning-based fault diagnosis methods are usually performed on the hypothesis that the training set and test set obey the same probability distribution, which is hard to satisfy under the actual working conditions. This paper proposes a novel multilayer domain adaptation (MLDA) method, which can diagnose the compound fault and single fault of multiple sizes simultaneously. A special designed residual network for the fault diagnosis task is pretrained to extract domain-invariant features. The multikernel maximum mean discrepancy (MK-MMD) and pseudo-label learning are adopted in multiple layers to take both marginal distributions and conditional distributions into consideration. A total of 12 transfer tasks in the fault diagnosis problem are conducted to verify the performance of MLDA. Through the comparisons of different signal processing methods, different parameter settings, and different models, it is proved that the proposed MLDA model can effectively extract domain-invariant features and achieve satisfying results.http://dx.doi.org/10.1155/2020/8873960 |
| spellingShingle | Bingru Yang Qi Li Liang Chen Changqing Shen Bearing Fault Diagnosis Based on Multilayer Domain Adaptation Shock and Vibration |
| title | Bearing Fault Diagnosis Based on Multilayer Domain Adaptation |
| title_full | Bearing Fault Diagnosis Based on Multilayer Domain Adaptation |
| title_fullStr | Bearing Fault Diagnosis Based on Multilayer Domain Adaptation |
| title_full_unstemmed | Bearing Fault Diagnosis Based on Multilayer Domain Adaptation |
| title_short | Bearing Fault Diagnosis Based on Multilayer Domain Adaptation |
| title_sort | bearing fault diagnosis based on multilayer domain adaptation |
| url | http://dx.doi.org/10.1155/2020/8873960 |
| work_keys_str_mv | AT bingruyang bearingfaultdiagnosisbasedonmultilayerdomainadaptation AT qili bearingfaultdiagnosisbasedonmultilayerdomainadaptation AT liangchen bearingfaultdiagnosisbasedonmultilayerdomainadaptation AT changqingshen bearingfaultdiagnosisbasedonmultilayerdomainadaptation |