A self-Adaptive CNN with PSO for bearing fault diagnosis
| Autor: | Jungan Chen, Jean Jiang, Xinnian Guo, Lizhe Tan |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Systems Science & Control Engineering, Vol 9, Iss 1, Pp 11-22 (2021) |
| Druh dokumentu: | article |
| ISSN: | 2164-2583 21642583 72586036 |
| DOI: | 10.1080/21642583.2020.1860153 |
| 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: | Directory of Open Access Journals |
| Externí odkaz: |
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