Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions
Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set an...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/6714520 |
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| _version_ | 1832564081078304768 |
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| author | Zhe Tong Wei Li Bo Zhang Meng Zhang |
| author_facet | Zhe Tong Wei Li Bo Zhang Meng Zhang |
| author_sort | Zhe Tong |
| collection | DOAJ |
| description | Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest-neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches. |
| format | Article |
| id | doaj-art-bb2428feccea4bee8ade8882720d0e68 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-bb2428feccea4bee8ade8882720d0e682025-02-03T01:11:49ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/67145206714520Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working ConditionsZhe Tong0Wei Li1Bo Zhang2Meng Zhang3School of Mechanical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaBearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest-neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.http://dx.doi.org/10.1155/2018/6714520 |
| spellingShingle | Zhe Tong Wei Li Bo Zhang Meng Zhang Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions Shock and Vibration |
| title | Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions |
| title_full | Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions |
| title_fullStr | Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions |
| title_full_unstemmed | Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions |
| title_short | Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions |
| title_sort | bearing fault diagnosis based on domain adaptation using transferable features under different working conditions |
| url | http://dx.doi.org/10.1155/2018/6714520 |
| work_keys_str_mv | AT zhetong bearingfaultdiagnosisbasedondomainadaptationusingtransferablefeaturesunderdifferentworkingconditions AT weili bearingfaultdiagnosisbasedondomainadaptationusingtransferablefeaturesunderdifferentworkingconditions AT bozhang bearingfaultdiagnosisbasedondomainadaptationusingtransferablefeaturesunderdifferentworkingconditions AT mengzhang bearingfaultdiagnosisbasedondomainadaptationusingtransferablefeaturesunderdifferentworkingconditions |