Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA
In response to the limitations of current fault diagnosis methods for mining rolling bearings, which suffer from limited feature extraction capabilities and poor generalization, a fault diagnosis method based on Superlet Transform (SLT) and OD-ConvNeXt-ELA was proposed. Built upon ConvNeXt-T, Batch...
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Editorial Department of Industry and Mine Automation
2024-12-01
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| Series: | Gong-kuang zidonghua |
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| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024080056 |
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| author | WU Xinzhong LUO Kang TANG Shoufeng HE Zexu CHEN Qi |
| author_facet | WU Xinzhong LUO Kang TANG Shoufeng HE Zexu CHEN Qi |
| author_sort | WU Xinzhong |
| collection | DOAJ |
| description | In response to the limitations of current fault diagnosis methods for mining rolling bearings, which suffer from limited feature extraction capabilities and poor generalization, a fault diagnosis method based on Superlet Transform (SLT) and OD-ConvNeXt-ELA was proposed. Built upon ConvNeXt-T, Batch Normalization (BN) technology was introduced to improve the network's generalization ability. Omni-dimensional Dynamic Convolution (ODConv) replaced the original depthwise separable convolution to enhance the adaptability of the network. Efficient Local Attention (ELA) was incorporated to focus the network on key feature locations. This formed the OD-ConvNeXt-ELA network model for fault diagnosis of mining rolling bearings. To fully leverage the image feature extraction ability of the OD-ConvNeXt-ELA model, SLT was used to convert the collected one-dimensional vibration signal of the rolling bearing into a two-dimensional time-frequency image, which was then input into the OD-ConvNeXt-ELA for model training. Fault diagnosis experiments were conducted using the bearing datasets from Case Western Reserve University (CWRU) and Paderborn University (PU). The results showed that for the CWRU bearing dataset under a single operating condition, the average fault diagnosis accuracy of OD-ConvNeXt-ELA was 99.65%, which was an improvement of 1.61% over ConvNeXt-T. For the CWRU bearing dataset under cross-operating conditions, the average fault diagnosis accuracy of OD-ConvNeXt-ELA was 87.50%, which was an improvement of 3.30% over ConvNeXt-T. For the PU bearing dataset under cross-operating conditions, the average fault diagnosis accuracy of OD-ConvNeXt-ELA was 89.33%, an improvement of 3.46% over ConvNeXt-T. The fault diagnosis method based on SLT and OD-ConvNeXt-ELA shows high accuracy and strong generalization ability under cross-bearing, cross-operating conditions, and noise interference. |
| format | Article |
| id | doaj-art-1cf0ea88e0f1446f91fcb795dcde5551 |
| institution | Kabale University |
| issn | 1671-251X |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-1cf0ea88e0f1446f91fcb795dcde55512025-01-23T02:17:44ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2024-12-01501212012710.13272/j.issn.1671-251x.2024080056Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELAWU Xinzhong0LUO Kang1TANG Shoufeng2HE Zexu3CHEN Qi4School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaIn response to the limitations of current fault diagnosis methods for mining rolling bearings, which suffer from limited feature extraction capabilities and poor generalization, a fault diagnosis method based on Superlet Transform (SLT) and OD-ConvNeXt-ELA was proposed. Built upon ConvNeXt-T, Batch Normalization (BN) technology was introduced to improve the network's generalization ability. Omni-dimensional Dynamic Convolution (ODConv) replaced the original depthwise separable convolution to enhance the adaptability of the network. Efficient Local Attention (ELA) was incorporated to focus the network on key feature locations. This formed the OD-ConvNeXt-ELA network model for fault diagnosis of mining rolling bearings. To fully leverage the image feature extraction ability of the OD-ConvNeXt-ELA model, SLT was used to convert the collected one-dimensional vibration signal of the rolling bearing into a two-dimensional time-frequency image, which was then input into the OD-ConvNeXt-ELA for model training. Fault diagnosis experiments were conducted using the bearing datasets from Case Western Reserve University (CWRU) and Paderborn University (PU). The results showed that for the CWRU bearing dataset under a single operating condition, the average fault diagnosis accuracy of OD-ConvNeXt-ELA was 99.65%, which was an improvement of 1.61% over ConvNeXt-T. For the CWRU bearing dataset under cross-operating conditions, the average fault diagnosis accuracy of OD-ConvNeXt-ELA was 87.50%, which was an improvement of 3.30% over ConvNeXt-T. For the PU bearing dataset under cross-operating conditions, the average fault diagnosis accuracy of OD-ConvNeXt-ELA was 89.33%, an improvement of 3.46% over ConvNeXt-T. The fault diagnosis method based on SLT and OD-ConvNeXt-ELA shows high accuracy and strong generalization ability under cross-bearing, cross-operating conditions, and noise interference.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024080056mining rolling bearingsfault diagnosisconvnextsuperlet transformfull-dimensional dynamic convolutionefficient local attention mechanism |
| spellingShingle | WU Xinzhong LUO Kang TANG Shoufeng HE Zexu CHEN Qi Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA Gong-kuang zidonghua mining rolling bearings fault diagnosis convnext superlet transform full-dimensional dynamic convolution efficient local attention mechanism |
| title | Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA |
| title_full | Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA |
| title_fullStr | Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA |
| title_full_unstemmed | Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA |
| title_short | Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA |
| title_sort | fault diagnosis of mining rolling bearings based on superlet transform and od convnext ela |
| topic | mining rolling bearings fault diagnosis convnext superlet transform full-dimensional dynamic convolution efficient local attention mechanism |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024080056 |
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