Stochastic program for selective maintenance decision considering diagnostics uncertainty of built-in test equipment

Built-in test equipment (BITE) applies to complex system more and more, for there is uncertainty in diagnostics, so ineffective maintenance is evitable. In order to get an optimal maintenance plan under BITE diagnostic uncertainty and resource constraint, it establishes a selective maintenance model considering diagnostics uncertainty of BITE, and puts forward a resolving algorithm. Firstly, it expounds the model assumption of selective maintenance considering BITE diagnostic uncertainty, and establishes a discrete, nonlinear, chance constrained programming model. Secondly, it designs a simulation based particle swarm optimization algorithm to solve the model, and it includes particle representation, fitness function, update methods and algorithm framework. Thirdly, it provides an example to verify the validity and feasibility of the model and solving algorithm. The model is suit to make feasible decision under BITE diagnostics uncertainty for manager.