Abstract: The existing testability verification methods take insufficient account of the equipment system structure and need a large number of fault sample size under both-sides' risk constraints. A fault sample size determination method based on hierarchical Bayesian network and posterior risk criteria is proposed. A hierarchical Bayesian network model of testability verification method is established according to the structure of an equipment system. In hierarchical Bayesian network model, the failure detection rate is used as the transmission parameter of the Bayesian network. Bayesian network reasoning algorithm is proposed to fully fuse the priori information of each level, and the joint prior distribution of fault detection rates is deduced based on fitting distribution selection method for skewness-kurtosis test. The posterior distribution is determined with the binomial data of the system. A fault sample size determination algorithm is established based on the posterior sample data and Bayesian posterior risk criteria, and is validated by an example. Compared with the classical and traditional Bayesian verification methods, the proposed method can reduce the sample size effectively under the same both-sides' risk constraints. Key

Key words: hierarchicalBayesiannetwork, posteriorriskcriteria, testability, testabilityverification, faultsamplesize, faultdetectionrate