A self-Adaptive CNN with PSO for bearing fault diagnosis
| Autor: | Xinnian Guo, Lizhe Tan, Jungan Chen, Jean Jiang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer science Computer Science::Neural and Evolutionary Computation Network structure Self adaptive 02 engineering and technology Fault (power engineering) Convolutional neural network law.invention Systems engineering TA168 020901 industrial engineering & automation Artificial Intelligence law 0202 electrical engineering electronic engineering information engineering bearing fault diagnosis Bearing (mechanical) Control engineering systems. Automatic machinery (General) business.industry Deep learning deep learning adaptive cnn Control and Systems Engineering TJ212-225 020201 artificial intelligence & image processing Artificial intelligence business Algorithm |
| Zdroj: | Systems Science & Control Engineering, Vol 9, Iss 1, Pp 11-22 (2021) |
| ISSN: | 2164-2583 |
| Popis: | Convolutional neural network (CNN) is now widely applied in bearing fault diagnosis, but the design of network structure or parameter tuning is time-consuming. To solve this problem, a particle swarm optimization (PSO) algorithm is used to optimize the network structure and a self-adaptive CNN is proposed in this paper. In the proposed method, a theoretical method is used to automatically determine the window size of short-time Fourier transform (STFT). To reduce the computation time, PSO is only applied to obtain the optimal key parameters in CNN with a small number of training samples and a small epoch number. To simplify the CNN structure, a fitness function considering the numbers of kernels and neuron nodes is used in PSO. According to the verification experiments for two well-known public datasets, the proposed method can get higher accuracy than other state-of-art methods. Furthermore, the parameters that are required to be input only involve the bearing parameters, so the proposed method can be applied in industry readily. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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