Bearing fault diagnosis method based on dual-channel feature fusion
Intelligent diagnosis method based on convolution neural network (CNN) has been widely used in bearing fault diagnosis. However, most existing diagnostic models rely on single-source information inputs, limiting their accuracy and reliability. To solve this limitation, this paper presents a rolling...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Electric Drive for Locomotives
2023-11-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2023.06.005 |
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| _version_ | 1850274412472827904 |
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| author | ZHANG Xiaoning ZHU Huilong XIN Liang YANG Muchen WANG Hao |
| author_facet | ZHANG Xiaoning ZHU Huilong XIN Liang YANG Muchen WANG Hao |
| author_sort | ZHANG Xiaoning |
| collection | DOAJ |
| description | Intelligent diagnosis method based on convolution neural network (CNN) has been widely used in bearing fault diagnosis. However, most existing diagnostic models rely on single-source information inputs, limiting their accuracy and reliability. To solve this limitation, this paper presents a rolling bearing fault diagnosis method based on dual-channel feature fusion. Firstly, the time-frequency analysis diagrams of rolling bearing vibration signals were constructed by using multiple Q-factor continuous Gabor wavelet transform (CMQGWT) and fast spectral coherence (Fast-SC), respectively. Subsequently, a CNN model with dual input channels was constructed, allowing for the fusion of deep time-frequency features extracted from each channel into a new feature at a feature fusion layer. Finally, the diagnosis results were output using a classifier. Through classification and recognition experiments involving single and compound faults in rolling bearings for high-speed trains, compared with the CNN model with a single input channel, the proposed model demonstrates superior diagnostic accuracy and robustness. |
| format | Article |
| id | doaj-art-5fd5ad40b59d46f193d0ac2d0e3c659b |
| institution | OA Journals |
| issn | 1000-128X |
| language | zho |
| publishDate | 2023-11-01 |
| publisher | Editorial Department of Electric Drive for Locomotives |
| record_format | Article |
| series | 机车电传动 |
| spelling | doaj-art-5fd5ad40b59d46f193d0ac2d0e3c659b2025-08-20T01:51:09ZzhoEditorial Department of Electric Drive for Locomotives机车电传动1000-128X2023-11-01394847324158Bearing fault diagnosis method based on dual-channel feature fusionZHANG XiaoningZHU HuilongXIN LiangYANG MuchenWANG HaoIntelligent diagnosis method based on convolution neural network (CNN) has been widely used in bearing fault diagnosis. However, most existing diagnostic models rely on single-source information inputs, limiting their accuracy and reliability. To solve this limitation, this paper presents a rolling bearing fault diagnosis method based on dual-channel feature fusion. Firstly, the time-frequency analysis diagrams of rolling bearing vibration signals were constructed by using multiple Q-factor continuous Gabor wavelet transform (CMQGWT) and fast spectral coherence (Fast-SC), respectively. Subsequently, a CNN model with dual input channels was constructed, allowing for the fusion of deep time-frequency features extracted from each channel into a new feature at a feature fusion layer. Finally, the diagnosis results were output using a classifier. Through classification and recognition experiments involving single and compound faults in rolling bearings for high-speed trains, compared with the CNN model with a single input channel, the proposed model demonstrates superior diagnostic accuracy and robustness.http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2023.06.005rolling bearingconvolution neural networkfeature fusionfault diagnosishigh-speed train |
| spellingShingle | ZHANG Xiaoning ZHU Huilong XIN Liang YANG Muchen WANG Hao Bearing fault diagnosis method based on dual-channel feature fusion 机车电传动 rolling bearing convolution neural network feature fusion fault diagnosis high-speed train |
| title | Bearing fault diagnosis method based on dual-channel feature fusion |
| title_full | Bearing fault diagnosis method based on dual-channel feature fusion |
| title_fullStr | Bearing fault diagnosis method based on dual-channel feature fusion |
| title_full_unstemmed | Bearing fault diagnosis method based on dual-channel feature fusion |
| title_short | Bearing fault diagnosis method based on dual-channel feature fusion |
| title_sort | bearing fault diagnosis method based on dual channel feature fusion |
| topic | rolling bearing convolution neural network feature fusion fault diagnosis high-speed train |
| url | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2023.06.005 |
| work_keys_str_mv | AT zhangxiaoning bearingfaultdiagnosismethodbasedondualchannelfeaturefusion AT zhuhuilong bearingfaultdiagnosismethodbasedondualchannelfeaturefusion AT xinliang bearingfaultdiagnosismethodbasedondualchannelfeaturefusion AT yangmuchen bearingfaultdiagnosismethodbasedondualchannelfeaturefusion AT wanghao bearingfaultdiagnosismethodbasedondualchannelfeaturefusion |