Abstract
To solve the problems that existing bearing fault diagnosis methods cannot adaptively select features and are difficult to deal with noise interference, an end-to-end fault diagnosis method is proposed based on attention CNN and BiLSTM (ACNN-BiLSTM). In the proposed method, the raw vibration acceleration signal of the bearing is taken as the input, the short-term spatial features are extracted through a one-dimensional wide convolutional neural network, and the batch normalization algorithm is used to improve the stability of the data distribution. Following, a convolutional block attention module is introduced to redistribute the weights between different feature dimensions, enhancing the model's attention to important features. Finally, the attention-weighted features are sent to BiLSTM for further feature extraction, and the softmax classifier is used for fault diagnosis. The proposed method is compared with advanced algorithms such as WCNN-BiGRU on the CWRU public dataset. The experimental results show that ACNN-BiLSTM has the highest accuracy, recall, and F1-Measure. Even under the extreme noise interference condition of SNR = 10 dB, ACNN-BiLSTM can achieve a diagnostic accuracy of 96.58%. In addition, the proposed method is also used for fault diagnosis of bearing measured data of the VALENIAN-PT500 test bench. The results show that the average diagnostic accuracy of ACNN-BiLSTM is up to 99.79%, which has strong generality and is superior to other advanced comparison methods.
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- CNN:
-
Convolutional neural networks
- BiLSTM:
-
Bidirectional long short-term memory network
- ACNN-BiLSTM:
-
Attention CNN and BiLSTM
- EMD:
-
Empirical mode decomposition
- WT:
-
Wavelet transform
- VMD:
-
Variational mode decomposition
- PCA:
-
Principal component analysis
- ICA:
-
Independent component analysis
- ANN:
-
Artificial neural network
- SVM:
-
Support vector machine
- PSO:
-
Particle swarm optimization
- CBAM:
-
Convolutional block attention module
- CAM:
-
Channel attention module
- SAM:
-
Spatial attention module
- BN:
-
Batch normalization
- ReLU:
-
Rectified linear unit
- MLP:
-
Multi-layer perceptron
- LSTM:
-
Long short-term memory network
- t-SNE:
-
T-distributed stochastic neighbor embedding
- SNR:
-
Signal-to-noise ratio
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This work was supported by Shanghai Pujiang Program (Grant No. 20PJ1404700).
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Guo, Y., Mao, J. & Zhao, M. Rolling Bearing Fault Diagnosis Method Based on Attention CNN and BiLSTM Network. Neural Process Lett 55, 3377–3410 (2023). https://doi.org/10.1007/s11063-022-11013-2
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DOI: https://doi.org/10.1007/s11063-022-11013-2