LAUSR

Fault diagnosis of high-speed train (HST) bogie plays an indispensable role in guaranteeing the safety and stability of the daily operation of HST. Up to now, the well-established fault diagnosis models of HST bogie, which are usually trained by supervised learning mechanism, require that the training and testing data satisfy the same probability distribution. Consequently, those methods could lose their efficacy when the probability distribution of the testing data is changed owing to the variation of HST running speed and the labeled data corresponding to the changed speed is absent. To address this, a stepwise adaptive convolutional network (SACN) is proposed to learn the domain-invariant features of vibration data in different speed domains. Moreover, a novel in-and-out stepwise transfer method is designed to deal with the scenario of continuous change in vehicle speed. The experimental tests of the proposed method are conducted using the dataset acquired by SIMPACK via the HST model CRH380 A, mainly addressing the single and compound failure classification problem of the three key components of HST bogie, namely, the air spring, antiyaw damper, and lateral damper. Overall, the proposed SACN achieves an average accuracy of 96.1%, demonstrating the remarkable performance of domain adaptation in performing fault diagnosis of HST bogie under variant running speeds.