An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder
Rotating machinery vibration signals are nonstationary and nonlinear under complicated operating conditions. It is meaningful to extract optimal features from raw signal and provide accurate fault diagnosis results. In order to resolve the nonlinear problem, an enhancement deep feature extraction me...
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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/10.1155/2018/6024874 |
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| _version_ | 1832563939325509632 |
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| author | Fengtao Wang Bosen Dun Xiaofei Liu Yuhang Xue Hongkun Li Qingkai Han |
| author_facet | Fengtao Wang Bosen Dun Xiaofei Liu Yuhang Xue Hongkun Li Qingkai Han |
| author_sort | Fengtao Wang |
| collection | DOAJ |
| description | Rotating machinery vibration signals are nonstationary and nonlinear under complicated operating conditions. It is meaningful to extract optimal features from raw signal and provide accurate fault diagnosis results. In order to resolve the nonlinear problem, an enhancement deep feature extraction method based on Gaussian radial basis kernel function and autoencoder (AE) is proposed. Firstly, kernel function is employed to enhance the feature learning capability, and a new AE is designed termed kernel AE (KAE). Subsequently, a deep neural network is constructed with one KAE and multiple AEs to extract inherent features layer by layer. Finally, softmax is adopted as the classifier to accurately identify different bearing faults, and error backpropagation algorithm is used to fine-tune the model parameters. Aircraft engine intershaft bearing vibration data are used to verify the method. The results confirm that the proposed method has a better feature extraction capability, requires fewer iterations, and has a higher accuracy than standard methods using a stacked AE. |
| format | Article |
| id | doaj-art-be48a7555ddd44c593f3c10443159992 |
| 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-be48a7555ddd44c593f3c104431599922025-02-03T01:12:10ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/60248746024874An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and AutoencoderFengtao Wang0Bosen Dun1Xiaofei Liu2Yuhang Xue3Hongkun Li4Qingkai Han5School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian 116024, ChinaRotating machinery vibration signals are nonstationary and nonlinear under complicated operating conditions. It is meaningful to extract optimal features from raw signal and provide accurate fault diagnosis results. In order to resolve the nonlinear problem, an enhancement deep feature extraction method based on Gaussian radial basis kernel function and autoencoder (AE) is proposed. Firstly, kernel function is employed to enhance the feature learning capability, and a new AE is designed termed kernel AE (KAE). Subsequently, a deep neural network is constructed with one KAE and multiple AEs to extract inherent features layer by layer. Finally, softmax is adopted as the classifier to accurately identify different bearing faults, and error backpropagation algorithm is used to fine-tune the model parameters. Aircraft engine intershaft bearing vibration data are used to verify the method. The results confirm that the proposed method has a better feature extraction capability, requires fewer iterations, and has a higher accuracy than standard methods using a stacked AE.http://dx.doi.org/10.1155/2018/6024874 |
| spellingShingle | Fengtao Wang Bosen Dun Xiaofei Liu Yuhang Xue Hongkun Li Qingkai Han An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder Shock and Vibration |
| title | An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder |
| title_full | An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder |
| title_fullStr | An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder |
| title_full_unstemmed | An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder |
| title_short | An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder |
| title_sort | enhancement deep feature extraction method for bearing fault diagnosis based on kernel function and autoencoder |
| url | http://dx.doi.org/10.1155/2018/6024874 |
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