Method of Behavior Analysis for Complex System Based on Hierarchical Bayesian Petri Net with Time Factor

For the problem that the status is proned to explosive growth when we try to model and analyse a complex system with huge scale,this paper proposed an Bayesian hierarchical Petri net model with the extending time factor(TF-HBPN),and based on this model proposed a recursive construction method and recursive abductive behavior analysis method.Firstly,our method creates top-level TF-HBPN according to the observed system's behavior and decomposes behavior analysis problem of complex systems through hierarchical recursion.Then it calculates the fault probability of the correct time sequence chain of fault events by recursive abductive reasoning.Finally,it calculates the bayesian probability of the event chain of system's behavior obtained by recursive abductive reasoning and time series analysis.The analysis results are compared with the right event chain to separate interference information.The experimental cases show that this method can model and analyze complex fault quickly and still can do system's behavior analysis and abductive reasoning with alarm missing.Compared with the general Petri nets,this method has a lower degree of modeling difficulty and is more concise and simple.