Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions
Abstract Bearing fault diagnosis is of great significance for ensuring the safety of rotating electromechanical equipment. A deep learning network framework for diagnosing bearing faults under multiple load conditions is proposed to address the problems of extracting a single feature scale from bear...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-96137-w |
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| author | Zhao Dengfeng Tian Chaoyang Fu Zhijun Zhong Yudong Hou Junjian He Wenbin |
| author_facet | Zhao Dengfeng Tian Chaoyang Fu Zhijun Zhong Yudong Hou Junjian He Wenbin |
| author_sort | Zhao Dengfeng |
| collection | DOAJ |
| description | Abstract Bearing fault diagnosis is of great significance for ensuring the safety of rotating electromechanical equipment. A deep learning network framework for diagnosing bearing faults under multiple load conditions is proposed to address the problems of extracting a single feature scale from bearing vibration timing signals, inability to simultaneously utilize spatial and bidirectional time features, and difficulty in obtaining sufficient training data under multiple working conditions. The first and second convolutional layers of a convolutional neural network (CNN) are used to simultaneously extract the spatio-temporal features from the bearing vibration signal and fuse them to obtain multi-scale spatiotemporal features. Based on this, BiLSTM is further applied to extract the bi-directional temporal correlation features of the input sequence. By introducing an attention mechanism (AM) to assign greater weights to critical spatio-temporal features, a new multi-scale deep learning network which integrates CNN, BiLSTM, and AM (MSCNN-BiLSTM-AM) network is proposed to obtain key bearing state features and accurate fault diagnose results. To further improve the adaptability of the network to different load conditions, the parameters of pretrained MSCNN-BiLSTM-AM network are applied to initialize the new task model parameters. After that, the new task diagnostic network is trained and validated under new load conditions by freezing the parameters of CNN, BiLSTM and AM layer, and fine-tuning the parameters of the fully connected layer and output layer. The experiments verify the excellent performance of the proposed method, while effectively solving the challenges of model training and fault diagnosis when there are insufficient training samples under multiple working conditions. |
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| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-a9d077f5f74a493383cc959367221d3c2025-08-20T02:17:53ZengNature PortfolioScientific Reports2045-23222025-04-0115111410.1038/s41598-025-96137-wMulti scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditionsZhao Dengfeng0Tian Chaoyang1Fu Zhijun2Zhong Yudong3Hou Junjian4He Wenbin5Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Mechanical and Electrical Engineering Institute, Zhengzhou University of Light IndustryHenan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Mechanical and Electrical Engineering Institute, Zhengzhou University of Light IndustryHenan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Mechanical and Electrical Engineering Institute, Zhengzhou University of Light IndustryHenan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Mechanical and Electrical Engineering Institute, Zhengzhou University of Light IndustryHenan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Mechanical and Electrical Engineering Institute, Zhengzhou University of Light IndustryHenan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Mechanical and Electrical Engineering Institute, Zhengzhou University of Light IndustryAbstract Bearing fault diagnosis is of great significance for ensuring the safety of rotating electromechanical equipment. A deep learning network framework for diagnosing bearing faults under multiple load conditions is proposed to address the problems of extracting a single feature scale from bearing vibration timing signals, inability to simultaneously utilize spatial and bidirectional time features, and difficulty in obtaining sufficient training data under multiple working conditions. The first and second convolutional layers of a convolutional neural network (CNN) are used to simultaneously extract the spatio-temporal features from the bearing vibration signal and fuse them to obtain multi-scale spatiotemporal features. Based on this, BiLSTM is further applied to extract the bi-directional temporal correlation features of the input sequence. By introducing an attention mechanism (AM) to assign greater weights to critical spatio-temporal features, a new multi-scale deep learning network which integrates CNN, BiLSTM, and AM (MSCNN-BiLSTM-AM) network is proposed to obtain key bearing state features and accurate fault diagnose results. To further improve the adaptability of the network to different load conditions, the parameters of pretrained MSCNN-BiLSTM-AM network are applied to initialize the new task model parameters. After that, the new task diagnostic network is trained and validated under new load conditions by freezing the parameters of CNN, BiLSTM and AM layer, and fine-tuning the parameters of the fully connected layer and output layer. The experiments verify the excellent performance of the proposed method, while effectively solving the challenges of model training and fault diagnosis when there are insufficient training samples under multiple working conditions.https://doi.org/10.1038/s41598-025-96137-wBearingsFault diagnosisCNNBiLSTMAMTransfer learning |
| spellingShingle | Zhao Dengfeng Tian Chaoyang Fu Zhijun Zhong Yudong Hou Junjian He Wenbin Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions Scientific Reports Bearings Fault diagnosis CNN BiLSTM AM Transfer learning |
| title | Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions |
| title_full | Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions |
| title_fullStr | Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions |
| title_full_unstemmed | Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions |
| title_short | Multi scale convolutional neural network combining BiLSTM and attention mechanism for bearing fault diagnosis under multiple working conditions |
| title_sort | multi scale convolutional neural network combining bilstm and attention mechanism for bearing fault diagnosis under multiple working conditions |
| topic | Bearings Fault diagnosis CNN BiLSTM AM Transfer learning |
| url | https://doi.org/10.1038/s41598-025-96137-w |
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