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Rolling Bearing Fault Diagnosis Method Based on Attention CNN and BiLSTM Network

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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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Abbreviations

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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Funding

This work was supported by Shanghai Pujiang Program (Grant No. 20PJ1404700).

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  1. Shanghai University of Engineering Science, Shanghai, 201620, China

    Yurong Guo, Jian Mao & Man Zhao

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  1. Yurong Guo
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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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