Bo Xie
Digital twin model, multiple unit trains, fault diagnosis, traction motor, transfer learning
Faced with the problem of fault diagnosis for traction motors in multiple units, this study constructed a digital twin (DT) model to simulate the generation of traction motor fault data, and used the CWRU bearing dataset to obtain physical data. This study designs a training network on the ground of deep learning and domain adaptation model (DAM), and conducts experimental data processing. The outcomes showcase that the DT model can effectively simulate normal and faulty bearing entities. The variation of acceleration in the x-direction of the normal bearing has a regularity, showing a certain periodic small amplitude vibration, with a vibration range of ± 10 m/s2. Compared to methods, such as deep domain confusion (DDC), the domain adaptive model method has higher accuracy. When the training frequency is 70, the domain adaptive model method has a maximum accuracy of 94.3%, which is 22.8% higher than the DDC method. The research method can effectively predict the traction motor faults of multiple unit trains.
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