- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | DU Jiahao, QIN Na, JIA Xinming, ZHANG Yiming, HUANG Deqing. Fault Diagnosis of Multiple Railway High Speed Train Bogies Based on Federated Learning[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 185-192. doi: 10.3969/j.issn.0258-2724.20220120
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To solve the problem of limited generalization ability of fault diagnosis model caused by the lack of sufficient fault data characteristics of single railway high-speed train bogie, and to realize the diagnosis of bogie faults of multiple railway high-speed trains, a global bogie fault diagnosis method based on federated learning is proposed in this work. Firstly, according to the bogie vibration signals of each railway, the multi-scale convolution fusion algorithm is conducted locally to extract and fuse the fault features at different scales, and the bogie fault diagnosis model is established locally. On the premise of not divulging data privacy, the fault diagnosis models of all railways are aggregated by the third party, the weights of model parameter are adjusted, the fault diagnosis models are optimized, and finally the global fault diagnosis model of bogie is jointly trained by multiple railways. The experiments show that under the federated learning framework, the fault diagnosis accuracy of the global bogie fault diagnosis model is reach more than 93% for the railway participating in federated modeling, and more than 75% for the railway not participating in, which provides a practical scheme for the ‘data island’ problem in railway transportation.
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