A Testability Modeling Study of Markov Process Incorporating Bayesian Network
For the current testability modeling,the testability index is inflated by the simple division of equipment operation state into normal and fault states,which ignores the equipment fault evolution characteristics.In this paper,we propose a testability modeling method of Markov process incorporating Bayesian network,which establishes a multi-state degradation model of components(normal,transition,fault)based on Markov stochastic process on the basis of fault and test uncertainty matrix,reduces the fuzzy interval caused by the uncertainty of failure mode under the traditional two states,and improves the accuracy of the model.Bayesian network inference is used to determine the multi-state test distribution function and optimize the values of false alarm rate and detection rate.Finally,a gantry-type automatic car washer was analyzed as a case study,and the detection rate of the system increased by 7.71%,the isolation rate increased by 11.55%,and the false alarm rate decreased by 9.51%,thus verifying the effectiveness of the proposed method.
万方数据
维普
