Fault Diagnosis Method for On-Board Interface Equipment of CTCS-3 Based on Temporal Knowledge Graph Completion

Chinese Train Control System level 3 (CTCS-3) train control onboard equipment plays a crucial role in ensuring train safety and improving operational efficiency. And the on-board interface equipment enables the interaction between the on-board Automatic Train Protection (ATP), ground equipment, drivers, and trains. While its fault accounts for a relatively high proportion of faults, therefore, this paper proposed a fault diagnosis method for on-board interface equipment based on temporal knowledge graph completion. Firstly, this method introduces temporal series to integrate travel logs and fault statistics, which extract fault phenomena and perform entity alignment, construct the temporal knowledge graph. Secondly, on this basis, we constructed a fault diagnosis network based on knowledge graph completion, which incorporates the Temporal-Translating Embedding (T - TransE) vectorization algorithm, Bidirectional Long Short-Term Memory (Bi-LSTM), and Self-Attention (SA) mechanism for temporal feature extraction. Finally, the T - TransE vectorization model was pretrained using the on-board interface equipment fault data from a railroad station in recent years for selecting the most effective temporal integration method. In order to validate the superiority of the proposed fault diagnosis method and the effectiveness of the data combination method, the diagnostic network without data combination and temporal relationship introduction, and other common fault diagnostic networks, were tested using the on -board fault data. Compared with the following fault diagnosis methods, the model proposed in this paper achieved the highest fault diagnostic accuracy of 96.8%.