Fault diagnosis method for bogie bearings of high-speed trains based on weighted-sparse subspace clustering

Abstract The bogie bearings are crucial components of the high-speed train transmission system, and any failure of them can severely impact the normal functioning of the vehicle. Nowadays, sparse subspace clustering (SSC) is an effective technology for diagnosing mechanical system faults. However, SSC is easily affected by noise. To address this issue, we introduce the weighted-SSC algorithm, which incorporates weighting coefficients to enhance the connections between similar sample points. Our approach involves extracting the fault characteristic parameters of the vibration signal through wavelet packet transform and singular value decomposition. Subsequently, these parameters are employed to construct corresponding weighted-sparse subspaces. We also examined the selection of a hyperparameter in the weighted coefficients. The resultant sparse representation vectors are then harnessed for the purpose of transductive semi-supervised learning clustering and diagnosing the specific type of fault. To validate the effectiveness of our proposed method, we designed and built a bearing experimental platform. We compare our method with existing clustering algorithms, including K -means, SSC, dimension reduction graph-based SSC, and ant colony optimization- K -means clustering algorithms. The results demonstrate that our proposed weighted-SSC method achieves higher accuracy in fault diagnosis than the other clustering algorithms.