In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module
To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4617 |
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| author | Junwei Zhu Xupeng Ouyang Zongkang Jiang Yanlong Xu Hongtao Xue Huiyu Yue Huayuan Feng |
| author_facet | Junwei Zhu Xupeng Ouyang Zongkang Jiang Yanlong Xu Hongtao Xue Huiyu Yue Huayuan Feng |
| author_sort | Junwei Zhu |
| collection | DOAJ |
| description | To address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. The main contributions are twofold: (1) A DCBA module is introduced to extract multi-scale features—including prominent, local, and average information—from grayscale images reconstructed from vibration signals across different domains; and (2) a two-stream network architecture is designed to learn complementary feature representations from time-domain and time–frequency-domain signals, which are fused through fully connected layers to improve diagnostic accuracy. Experimental results demonstrate that the proposed method achieves high recognition accuracy under various working speeds, loads, and road surfaces. Comparative studies with SENet, ECANet, CBAM, and single-stream 2DCNN models confirm its superior performance and robustness. The integration of DCBA with dual-domain feature learning effectively enhances fault feature extraction under complex operating conditions. |
| format | Article |
| id | doaj-art-78ea00af09c34d90a32f854505eb3d97 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-78ea00af09c34d90a32f854505eb3d972025-08-20T04:00:49ZengMDPI AGSensors1424-82202025-07-012515461710.3390/s25154617In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA ModuleJunwei Zhu0Xupeng Ouyang1Zongkang Jiang2Yanlong Xu3Hongtao Xue4Huiyu Yue5Huayuan Feng6School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaTo address the challenge of fault diagnosis for in-wheel motors in four-wheel independent driving systems under variable driving conditions and harsh environments, this paper proposes a novel method based on two-stream 2DCNNs (two-dimensional convolutional neural networks) with a DCBA (depthwise convolution block attention) module. The main contributions are twofold: (1) A DCBA module is introduced to extract multi-scale features—including prominent, local, and average information—from grayscale images reconstructed from vibration signals across different domains; and (2) a two-stream network architecture is designed to learn complementary feature representations from time-domain and time–frequency-domain signals, which are fused through fully connected layers to improve diagnostic accuracy. Experimental results demonstrate that the proposed method achieves high recognition accuracy under various working speeds, loads, and road surfaces. Comparative studies with SENet, ECANet, CBAM, and single-stream 2DCNN models confirm its superior performance and robustness. The integration of DCBA with dual-domain feature learning effectively enhances fault feature extraction under complex operating conditions.https://www.mdpi.com/1424-8220/25/15/4617in-wheel motorfault diagnosistwo-stream 2DCNNsdepthwise convolution block attention |
| spellingShingle | Junwei Zhu Xupeng Ouyang Zongkang Jiang Yanlong Xu Hongtao Xue Huiyu Yue Huayuan Feng In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module Sensors in-wheel motor fault diagnosis two-stream 2DCNNs depthwise convolution block attention |
| title | In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module |
| title_full | In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module |
| title_fullStr | In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module |
| title_full_unstemmed | In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module |
| title_short | In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module |
| title_sort | in wheel motor fault diagnosis method based on two stream 2dcnns with dcba module |
| topic | in-wheel motor fault diagnosis two-stream 2DCNNs depthwise convolution block attention |
| url | https://www.mdpi.com/1424-8220/25/15/4617 |
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