A truncated test scheme design method for success-failure in-orbit tests

Based on the success-failure test feature of in-orbit tests (IOTs) for typical space equipment, this paper presents a method for designing a truncated test scheme for success-failure in-orbit tests. With this method, a small upper boundary of the sample size for the IOT verification test can be obtained before the test starts. The method introduces the truncated Bayes-sequential mesh test (SMT) method into the design of the IOT verification test scheme and greatly compresses the continuous test area by incorporating optimization theory, resulting in a smaller upper limit of the IOT sample size. First, this paper derives a specific calculation formula for the BayesSMT critical line. Second, the Markov chain model is adopted to calculate the occurrence probabilities of each acceptance and rejection point through state transition. Finally, an optimal truncated test optimization algorithm based on the augmented lagrangian genetic algorithm is proposed. Simulation tests show that, compared with the classical single sampling method, the truncated sequential probability ratio test method, the truncated SMT method, and the truncated Bayes-SMT method based on step-by-step calculation, the method presented in this paper can be used to obtain a sequential test scheme with smaller truncated sample size.

Automated summary

This paper proposes a method for designing a truncated test scheme for success-failure in-orbit tests, which compresses the continuous test area and obtains a smaller upper limit of the IOT sample size by introducing the truncated Bayes-sequential mesh test method and incorporating optimization theory. The specific calculation formula for the BayesSMT critical line is derived, and the occurrence probabilities of each acceptance and rejection point are calculated using the Markov chain model. Finally, an optimal truncated test optimization algorithm based on the augmented lagrangian genetic algorithm is proposed.