Abstract: The existing testability verification scheme, which is based on the sequential posterior odds test, has insufficient consideration of the fuzzy parameter space between the testability design indicators, and it fails to make full use of the testability multi-source prior information. So a testability verification scheme based on the optimized sequential posterior odds test and D-S evidence theory is proposed. Firstly, when the fuzzy parameter space between testability design indicators is taken into consideration, a three-parameter space complex hypothesis is constructed. At the same time, the decision rules of the hypothesis test, decision factors and thresholds are proposed based on Bayes theory. Secondly, the parameter space composed of testability indicators is used as the identification framework, and the basic trust distribution functions based on multi-source prior information, such as expert information and testability data are constructed. Meanwhile, an optimized sequential verification scheme that integrates multi-source prior information is established. Finally, the research is carried out with examples, and the empirical results are compared with the classic verification scheme, the traditional Bayes verification scheme, the sequential probably rate test scheme, and the sequential posterior odds test scheme. The results show that the proposed scheme considers the fuzzy parameter space and fully integrates multi-source prior information, so it can effectively solve the problem of processing fuzzy parameter space, and the average fault sample size determined is better than other schemes in the parameter space of decision support.

Key words: sequential posterior odds test(SPOT), testability verification, fuzzy parameter space, prior information, D-S evidence theory, fault sample size