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.