Fault Diagnosis Based on Multiscale Texture Features of Cable Terminal on EMU of High-Speed Railway

The problem to identify the type of source generating discharges detected during campaigns of partial discharge (PD) measurements in cable terminal is of utmost importance in the quality control and state detection of the power supply system of a high-speed railway train. The purpose of this article is to provide a novel PD feature and a potential tool for cable terminal PD identification. The traditional PD type of high-speed train cable terminal is usually identified by PD test parameters, such as amplitude, PD times, and discharge energy. These features contain limited information and are easily affected by external factors, resulting in large numerical fluctuations, making the reliability of the test results low, which makes the operation, maintenance, and repair decision of high-speed train cables very difficult. Therefore, this article proposes a method that can extract rich feature information from PD data and distinguish PD types accurately and reliably by combining with a classification algorithm. First, to make full use of the characteristics of PD data, the single-scale PRPD images are transformed into multiscale PRPD images by using image pyramid theory. Then, the first-order texture statistics, the second-order texture statistics, and the higher order texture statistics are extracted from the multiscale PRPD space. Finally, the input features are optimized and classified by random forest and compared with other feature extraction algorithms. The results show that this method can identify PD type accurately and reliably, and the recognition accuracy is better than that of a single PD discharge feature.