Fault Diagnosis in Partially Observable Petri Nets with Quantum Bayesian Learning

Abstract

This paper investigates the quantum Bayesian probability estimation of fault diagnosis based on Partially Observable Petri Nets (POPN) for a liquid-propellant rocket engine system. To solve the problem of a poor environment, a complex structure, and limited observable information in the liquid-propellant rocket engine system, a method of fault diagnosis based on POPN and quantum Bayesian probability estimation is proposed. According to the operating state and key actions of the system model, the places and transitions are set, and the unobservable key actions will become unobservable transitions. Combined with the trigger relationship of the transitions, a POPN model is established. All path estimation system states that satisfy the observable transition sequence information are traversed. If the diagnosis result is a possible failure, we establish a quantum Bayesian Petri net model corresponding to the failure transition, manually adjust the quantum parameters to calculate the quantum probability of the failure transition, and determine the system failure state. Finally, the model of the start-up process of the engine system based on the POPN is built to verify the effectiveness of the algorithm with the data in the simulation experiment.