An Urban Rail Signal Fault Diagnosis System Based on Knowledge Model

At present, urban rail signal maintenance system can only alarm a single fault source, and can not quickly locate the cause of fault and guide operation and maintenance personnel to deal with the fault. However, urban rail signal system has a large variety of faults, complex diagnosis and analysis logic, customized development of fault diagnosis procedures for different scenarios can not, quickly respond to the needs of operation and maintenance, and the cost is high. To solve this problem, this paper develops an information-based and platform-based urban rail signal fault diagnosis system based on knowledge model to realize signal system fault knowledge modeling, fault diagnosis semantic correlation and fault diagnosis process modeling. In order to realize the complete reasoning of signal system fault diagnosis, OWL DL(ontology web language description logic) is used to model knowledge, extract and describe the analysis logic of signal system fault diagnosis, and establish the knowledge model. The operation state of system equipment is used to match the fault cause, and the mapping from signal system equipment state space to fault cause space is used to realize the fault self-diagnosis of signal system equipment, so as to provide decision support for the production, operation and maintenance management of signal system equipment. Application results show that it can reduce the operation safety accident rate by 15% and improve the fault handling efficiency by 20%.