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

He Tian; Huaicong Fan; Mingwen Feng; Ranran Cao; Dong Li

doi:10.3390/s23146508

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

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

Authors

He Tian^{1

2}, Huaicong Fan^{1

2}, Mingwen Feng^{1

2}, Ranran Cao^{1

2}, Dong Li^{3

4}

Affiliations

¹ National Demonstration Center for Experimental Mechanical and Electrical Engineering Education, Tianjin University of Technology, Tianjin 300384, China.
² Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China.
³ School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China.
⁴ Tianjin Key Laboratory for Control Theory & Applications in Complicated Industry Systems, Tianjin 300000, China.

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.

Grants and funding

This research was funded by the State Grid Tianjin Electric Power Company Science and Technology Project, grant number KJ21-1-21, the Tianjin Postgraduate Scientific Research Innovation Project, grant number 2022SKYZ070 and the Tianjin University of Technology 2022 School-Level Postgraduate Scientific Research Innovation Practice Project, grant number YJ2209.