Deep-PCA and MSPCA based fault diagnosis of high-speed train traction systems under missing data conditions

The high-speed train power traction system operates for extended periods of time in complex environments with high temperatures and vibrations, making the system susceptible to various types of incipient faults. In addition, coupled with missing sensor data under complex operating conditions, the diagnosis of incipient faults under practical conditions becomes even more challenging. To address the aforementioned issues, this paper proposes an innovative data imputation method that combines kernel functions with the modified Akima (Makima) interpolation algorithm. This method can effectively address the scenario of a large number of sensor signals continuously missing in high-speed train traction systems. Expanding on this, a real-time incipient fault diagnosis framework is proposed, which combines Deep-Principal Component Analysis (Deep-PCA) with Multi-scale Principal Component Analysis (MSPCA). This framework enhances the capability to extract fault features from both horizontal and vertical perspectives, thereby improving the accuracy of fault detection. Furthermore, the continuous wavelet transform (CWT) is employed to amplify fault-related information within the dataset. This enhanced dataset, combined with a fault isolation criterion based on the reconstructed cumulative contribution rate, enables the achievement of precise fault isolation.