A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN
Fault diagnosis is critical to maintaining the performance of rotating machinery and ensuring the safe operation of the equipment. Convolutional neural networks (CNNs) have recently shown great potential with excellent automatic feature learning and nonlinear mapping abilities in the field of rotati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10265257/ |
| _version_ | 1797663104283181056 |
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| author | Hongxing Wang Hua Zhu Huafeng Li |
| author_facet | Hongxing Wang Hua Zhu Huafeng Li |
| author_sort | Hongxing Wang |
| collection | DOAJ |
| description | Fault diagnosis is critical to maintaining the performance of rotating machinery and ensuring the safe operation of the equipment. Convolutional neural networks (CNNs) have recently shown great potential with excellent automatic feature learning and nonlinear mapping abilities in the field of rotating machinery fault diagnosis. However, the CNN-based methods still suffer from some defects, such as inadequate data utilization and uneconomical computational efficiency, which limit the further improvement of diagnosis performance. Therefore, this paper proposes a fault diagnosis method based on multi-sensor fusion and Convolutional Neural Network with Efficient Channel Attention (ECA-CNN). First, multi-sensor vibration signals are sampled, converted, and channel fused into multi-channel images with rich and comprehensive features. Then, the efficient channel attention mechanism is introduced into CNN to increase the feature learning ability by adaptively scoring and assigning weights to the channel features. The ECA-CNN is proposed to learn representative fault features from multi-sensor fusion data to achieve fault identification. Finally, two experimental cases on the bearing and gearbox datasets prove that the proposed method has excellent performance, strong generalization capability, and high computational efficiency. |
| first_indexed | 2024-03-11T19:09:45Z |
| format | Article |
| id | doaj.art-fd6899b9a7c54aa8919bf130be892f27 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T19:09:45Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fd6899b9a7c54aa8919bf130be892f272023-10-09T23:02:00ZengIEEEIEEE Access2169-35362023-01-011110644310645510.1109/ACCESS.2023.332006510265257A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNNHongxing Wang0https://orcid.org/0000-0003-0177-2072Hua Zhu1https://orcid.org/0009-0001-7227-5615Huafeng Li2https://orcid.org/0009-0006-3304-8635State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaState Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaState Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaFault diagnosis is critical to maintaining the performance of rotating machinery and ensuring the safe operation of the equipment. Convolutional neural networks (CNNs) have recently shown great potential with excellent automatic feature learning and nonlinear mapping abilities in the field of rotating machinery fault diagnosis. However, the CNN-based methods still suffer from some defects, such as inadequate data utilization and uneconomical computational efficiency, which limit the further improvement of diagnosis performance. Therefore, this paper proposes a fault diagnosis method based on multi-sensor fusion and Convolutional Neural Network with Efficient Channel Attention (ECA-CNN). First, multi-sensor vibration signals are sampled, converted, and channel fused into multi-channel images with rich and comprehensive features. Then, the efficient channel attention mechanism is introduced into CNN to increase the feature learning ability by adaptively scoring and assigning weights to the channel features. The ECA-CNN is proposed to learn representative fault features from multi-sensor fusion data to achieve fault identification. Finally, two experimental cases on the bearing and gearbox datasets prove that the proposed method has excellent performance, strong generalization capability, and high computational efficiency.https://ieeexplore.ieee.org/document/10265257/Fault diagnosismulti-sensor fusionconvolutional neural networkschannel attention mechanism |
| spellingShingle | Hongxing Wang Hua Zhu Huafeng Li A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN IEEE Access Fault diagnosis multi-sensor fusion convolutional neural networks channel attention mechanism |
| title | A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN |
| title_full | A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN |
| title_fullStr | A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN |
| title_full_unstemmed | A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN |
| title_short | A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN |
| title_sort | rotating machinery fault diagnosis method based on multi sensor fusion and eca cnn |
| topic | Fault diagnosis multi-sensor fusion convolutional neural networks channel attention mechanism |
| url | https://ieeexplore.ieee.org/document/10265257/ |
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