Design of Abnormal Event Diagnosis System Based on Petri Net for High-Speed Vehicle

High-speed vehicle adopt different control strategies for different fault events in the event of external environment disturbance and servo failure event leading to state instability. In order to enable the vehicle to identify the type of event that causes the abnormal state autonomously, and accordingly call the corresponding processing strategy, this paper proposes a decision system based on decision network for vehicle anomaly diagnosis. Based on the Petri net method, the abnormal event diagnosis system of the vehicle is constructed. By analyzing the influence of the different event on the flight state, the system can make the vehicle autonomously locate the event types that cause the abnormal state quickly by using the matrix reasoning ability of Petri net, with sensor reading of each state quantity as input and event type as output, provides the basis for the follow-up control behavior. Finally, a RLV reentry section is taken as an example. After the various types of faults are injected into it, the correctness of the event diagnosis system is verified by C++ software. The result proves that the diagnosis system can diagnose and distinguish different types of events correctly.