Diagnostic Strategy Optimization Method under Unreliable Test

In order to solve the problem of low diagnosis accuracy and high false alarm rate in the fault diagnosis of complex systems, the research of diagnosis strategy optimization method based on information entropy algorithm under unreliable test conditions is studied. A heuristic function that comprehensively considers the fault detection capability, information volume, test cost, and test result trustworthiness of the test point is established. The information entropy algorithm is used to generate the diagnostic strategy. Finally, the Apollo detection system example is used to verify the superiority of the algorithm. Theoretical and experimental results show that the average test cost of the information entropy algorithm is lower than that of the $AO^{\ast}$ algorithm, and the calculation time is within an acceptable range. Therefore, it can be used to test the design of diagnostic strategies under unreliable conditions.