Dynamic Bayesian networks in electronic equipment health diagnosis

Abstract

Bayesian network is the main research method in the field of artificial intelligence for uncertainty problem representation and processing of and health grading evaluation is one of the important technology in health management. Through the analysis of different models and study methods of Bayesian theory, combining the characteristics of the three-state dividing of system, the three states of dynamic Bayesian network health evaluation method is put forward, which calculates the dynamic Bayesian network using the hidden Markov model. Then EM algorithm and CRLA algorithm for dynamic Bayesian networks parameter learning are studied. Finally, based on Pspice simulation software and the DBN software toolbox of Matlab, the three-stage amplifier circuit Bayesian evaluation model of the three health states is built, and the corresponding application instructions and results are obtained. Thus the correctness and feasibility of the related methods put forward in this paper are verified.