Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion
Given the problems of low accuracy and complex process steps currently faced by the field of fault diagnosis, a fault diagnosis method based on multi-sensor data weighted fusion (MSDWF) combined with depth-wise separable convolutions (DWSC) is proposed. The method takes into account the temporal and...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7640 |
| _version_ | 1797414635382505472 |
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| author | Tong Wang Xin Xu Hongxia Pan Xuefang Chang Taotao Yuan Xu Zhang Hongzhao Xu |
| author_facet | Tong Wang Xin Xu Hongxia Pan Xuefang Chang Taotao Yuan Xu Zhang Hongzhao Xu |
| author_sort | Tong Wang |
| collection | DOAJ |
| description | Given the problems of low accuracy and complex process steps currently faced by the field of fault diagnosis, a fault diagnosis method based on multi-sensor data weighted fusion (MSDWF) combined with depth-wise separable convolutions (DWSC) is proposed. The method takes into account the temporal and spatial information contained in multi-sensor data and can realize end-to-end bearing fault diagnosis. MSDWF is committed to comprehensively characterizing the state information of bearings, and the weighted operation of the multi-sensor data is to establish the interactive information to tap into the inline relationship in the data; DWSC equipped with residual connection is used to realize the decoupling of the channel and spatial correlation of the data. In order to verify the proposed method, the data obtained by a different number of sensors with weighted and unweighted states are used as the input of DWSC, respectively, for comparison, and finally, the effectiveness of MSDWF is verified. Through the comparison between different fault diagnosis methods, the method based on MSDWF and DWSC shows better stability and higher accuracy. Finally, when facing different experimental datasets, the method has similar performance, which shows the stability of the method on different datasets. |
| first_indexed | 2024-03-09T05:36:16Z |
| format | Article |
| id | doaj.art-0d8742a5444c42e4a30b11fd9b437e61 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:36:16Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0d8742a5444c42e4a30b11fd9b437e612023-12-03T12:28:30ZengMDPI AGApplied Sciences2076-34172022-07-011215764010.3390/app12157640Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted FusionTong Wang0Xin Xu1Hongxia Pan2Xuefang Chang3Taotao Yuan4Xu Zhang5Hongzhao Xu6School of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaGiven the problems of low accuracy and complex process steps currently faced by the field of fault diagnosis, a fault diagnosis method based on multi-sensor data weighted fusion (MSDWF) combined with depth-wise separable convolutions (DWSC) is proposed. The method takes into account the temporal and spatial information contained in multi-sensor data and can realize end-to-end bearing fault diagnosis. MSDWF is committed to comprehensively characterizing the state information of bearings, and the weighted operation of the multi-sensor data is to establish the interactive information to tap into the inline relationship in the data; DWSC equipped with residual connection is used to realize the decoupling of the channel and spatial correlation of the data. In order to verify the proposed method, the data obtained by a different number of sensors with weighted and unweighted states are used as the input of DWSC, respectively, for comparison, and finally, the effectiveness of MSDWF is verified. Through the comparison between different fault diagnosis methods, the method based on MSDWF and DWSC shows better stability and higher accuracy. Finally, when facing different experimental datasets, the method has similar performance, which shows the stability of the method on different datasets.https://www.mdpi.com/2076-3417/12/15/7640bearing fault diagnosisdepth-wise separable convolutionsmulti-sensor data fusiondata weighting |
| spellingShingle | Tong Wang Xin Xu Hongxia Pan Xuefang Chang Taotao Yuan Xu Zhang Hongzhao Xu Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion Applied Sciences bearing fault diagnosis depth-wise separable convolutions multi-sensor data fusion data weighting |
| title | Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion |
| title_full | Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion |
| title_fullStr | Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion |
| title_full_unstemmed | Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion |
| title_short | Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion |
| title_sort | rolling bearing fault diagnosis based on depth wise separable convolutions with multi sensor data weighted fusion |
| topic | bearing fault diagnosis depth-wise separable convolutions multi-sensor data fusion data weighting |
| url | https://www.mdpi.com/2076-3417/12/15/7640 |
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