Air conditioning reliability analysis based on dynamic Bayesian network and Markov model

With the popularization of the air conditioning, its reliability during operation has gradually become a focus of attention. However, due to the uncertainty in the reliability analysis process, the accuracy of the results will be affected. To overcome this challenge, a method for air conditioner reliability analysis combining Dynamic Bayesian Network (DBN) and Markov Model (MM) is proposed. Firstly, orthogonal defect classification (ODC) is used to statistic and analyze the defect data of the air conditioning system, and the network structure of the DBN is determined based on the results of the analysis. Then, the state transfer probability of each node is obtained by MM, and then the reliability, steady state availability, and maintainability of the air conditioning system are analyzed. Finally, the effectiveness of the method is verified by a case study of air conditioning failure data. The results show that the steady state availability of the air conditioning system in this case is 0.996.