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. 2023 Jul 19;23(14):6508.
doi: 10.3390/s23146508.

Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network

He Tian  1   2 , Huaicong Fan  1   2 , Mingwen Feng  1   2 , Ranran Cao  1   2 , Dong Li  3   4
Affiliations

Fault Diagnosis of Rolling Bearing Based on HPSO Algorithm Optimized CNN-LSTM Neural Network

He Tian et al. Sensors (Basel). .

Abstract

The quality of rolling bearings is vital for the working state and rotation accuracy of the shaft. Timely and accurately acquiring bearing status and early fault diagnosis can effectively prevent losses, making it highly practical. To improve the accuracy of bearing fault diagnosis, this paper proposes a CNN-LSTM bearing fault diagnosis model optimized by hybrid particle swarm optimization (HPSO). The HPSO algorithm has a strong global optimization ability and can effectively solve nonlinear and multivariate optimization problems. It is used to optimize and match the parameters of the CNN-LSTM model and dynamically find the optimal value of the parameters. This model overcomes the problem that the parameters of the CNN-LSTM model depend on empirical settings and cannot be adjusted dynamically. This model is used for bearing fault diagnosis, and the accuracy rate of fault diagnosis classification reaches 99.2%. Compared with the traditional CNN, LSTM, and CNN-LSTM models, the accuracy rates are increased by 6.6%, 9.2%, and 5%, respectively. At the same time, comparing the models with different optimization parameters shows that the model proposed in this paper has the highest accuracy. The experimental results verified the superiority of the HPSO algorithm to optimize model parameters and the feasibility and accuracy of the HPSO-CNN-LSTM model for bearing fault diagnosis.

Keywords: convolutional neural network; hybrid particle swarm optimization; intelligent fault diagnosis; long short-term memory; rolling bearing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
LSTM structure.
Figure 2
Figure 2
PSO, CPSO and HPSO optimal individual fitness curves.
Figure 3
Figure 3
Fault diagnosis flow chart of the HPSO-CNN-LSTM model.
Figure 4
Figure 4
Case Western Reserve University bearing experimental data acquisition equipment.
Figure 5
Figure 5
Confusion matrix for the test data.
Figure 6
Figure 6
2D scatter diagram.
Figure 7
Figure 7
Accuracy curves of training sets of different network models.
Figure 8
Figure 8
Loss curves of training sets of different network models.
Figure 9
Figure 9
Classification accuracy of the network model with different optimization parameters.
See this image and copyright information in PMC

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