KICA-DPCA-Based Fault Detection of High-Speed Train Traction Motor Bearings - Yunkai Wu; Yu Tian; Yang Zhou (Machines)
The signals of high-speed train traction motor bearings contain strong noise and exhibit non-linear and non-Gaussian characteristics. To address the aforementioned issues, this paper proposes a method that combines Kernel Independent Component Analysis and Deep Principal Component Analysis (KICA-DPCA) to improve the accuracy of bearing fault detection. Firstly, DPCA is utilized to thoroughly extract fault information from the dataset while simultaneously achieving the purpose of noise reduction. Secondly, KICA is combined to project the data into a high-dimensional feature space and extract independent components, thereby separating the data into two groups following Gaussian and non-Gaussian distributions. Furthermore, the occurrence of bearing faults is determined by evaluating the statistical residuals against the predefined threshold. Finally, the proposed algorithm is validated on both simulation data from the Traction Drive Control System-Fault Injection Benchmark (TDCS-FIB) platform and experimental data from the Case Western Reserve University bearing fault dataset. Comparative tests are conducted using the false alarm rate (FAR) and fault detection rate (FDR) as evaluation metrics, which fully demonstrate the effectiveness and superiority of the proposed method.