Research on construction method of fault knowledge graph of CTCS on-board equipment

As the core of the CTCS, the on-board equipment has the characteristics of complex structure and close connection between modules. During the operation, if a fault occurs, it will directly affect the safe and efficient operation of the train. In order to make the maintenance personnel accurately grasp the fault situation of the on-board equipment, it is of great practical significance to study the fault maintenance log containing rich experience information by means of intelligent means. By analyzing the characteristics of such logs, the paper proposed a combination of top-down and bottom-up methods to construct a fault knowledge graph of in-vehicle equipment. Based on the entity relationship conversion of vehicle fault maintenance log, the entity recognition of semi-structured data was regarded as a key phrase extraction problem. A method combining word vector, topic model and dictionary features was proposed to obtain key phrases firstly, then use Bi-gram model to extract key phrases. The fault words were spliced into candidate fault phrases, and the one with the highest score was selected as the required fault entity. For the relationship between entities, the method based on pattern matching was used to construct the on-board equipment fault relationship template and mine the relationship between the faults. To solve the redundancy and errors of entities, the cosine similarity calculation between entity vectors was used and entity fusion was realized through threshold setting, so as to complete the knowledge mining of on-board equipment failures. Finally, an experiment was carried out using the 2019～2020 on-board fault maintenance log of a railway bureau as the data, and 339 fault entities and 734 fault relationships were extracted accumulatively. Based on the research, a fault knowledge graph of on-board equipment was constructed, and the graph was constructed and displayed and retrieved in a visual way. The relationship between equipment faults effectively improves the knowledge discovery ability of the on-board fault log and facilitates the maintenance of on-board equipment faults. © 2023, Central South University Press. All rights reserved.