Health-state evaluation for aerospace systems

Purpose - The purpose of this paper is to evaluate the health-states of unit under test (UUT) in aerospace systems by means of unreliable test outcomes, and the evaluation results can provide a guide for engineers to carry out proper maintenance prior to total failure. Design/methodology/approach - In this paper, the authors formulate the health-state evaluation (HSE) problem with unreliable test outcomes based on Bayes rule, and develop the Lagrangian relaxation and adaptive genetic algorithm (LRAGA) to solve it. The solution scheme can be viewed as a two-level coordinated solution framework for the HSE problem. At the top level, the Lagrange multipliers are updated by using AGA. At the bottom level, each of the sub-problems is solved by using AGA. Findings - The experimental results show that the HSE model appears promising and the LRAGA can obtain the higher quality solution and converge to it at a faster rate than conventional methods (i.e. Lagrangian relaxation (LR), genetic algorithm (GA), simulated annealing (SA) and Lagrangian relaxation and genetic algorithm (LRGA). Research limitations/implications - The proposed method for the HSE problem of large-scale systems which include thousands of faults and tests needs to be verified further. Practical implications - The HSE results for aerospace systems can help engineers to carry out a schedule for prompt maintenance prior to UUTs' failure, to avoid the consequences of total failure. It is important to improve aerospace systems' safety, reliability, maintainability, affordability, and reduce life cycle cost. Originality/value - This paper constructs the HSE model with unreliable test outcomes based on the Bayes rule and proposes a method based on LRAGA to solve the HSE problem.