Defects Detection of Onboard Cable Termination in EMUs Using Partial Discharge Measurement and SDP-DL Framework

Termination is the most important part of onboard cable, which plays a vital role in ensuring the continuous and reliable power supply to electrical multiple units (EMUs). The maintenance time of EMUs is limited, so the time left for partial discharge (PD) measurement of onboard cable termination is very short, leading to the obvious decreasing of detection accuracy. For addressing this issue, this article proposed a symmetrized dot pattern (SDP)-deep learning (DL) framework to detect the insulation defects using a time-series PD pulse signal. First, a PD measurement platform and the experimental samples were prepared in laboratory to obtain the time-series PD pulse signals of four typical insulation defects. Then, the time-series PD signals were converted into SDP images using the proposed parameter optimization method. Finally, the SDP-DL framework was proposed, and three specific and typical methods were utilized, i.e., convolutional neural network (CNN), stacked auto encoder (SAE), and deep belief network (DBN). The results show that the performance of the SDP-DBN method is the best, and the insulation defects can be detected with an accuracy of 96.1%. In addition, the visualization ability of data increases after SDP transformation of the original PD time-series signal.