Termination is the most important part of on-board cable, which plays a vital role in ensuring the continuous and reliable power supply to EMUs (electrical multiple units). The maintenance time of EMUs is limited, so the time left for PD (partial discharge) measurement of on-board cable termination is very short, leading to the obvious decreasing of detection accuracy. For addressing this issue, this paper proposed an SDP (symmetrized dot pattern)–DL (deep learning) framework to detect the insulation defects using time series PD pulse signal. First, a PD measurement platform and 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., CNN (convolutional neural network), SAE (stacked auto encoder) and DBN (deep belief network). The results show that the performance of SDP-DBN method is the best, and the insulation defects can be detected with accuracy of 96.1%. In addition, the visualization ability of data increases after SDP transformation of the original PD time series signal.