Test selection and optimization for PHM based on failure evolution mechanism model

The test selection and optimization (TSO) can improve the abilities of fault diagnosis, prognosis and health-state evaluation for prognostics and health management (PHM) systems. Traditionally, TSO mainly focuses on fault detection and isolation, but they cannot provide an effective guide for the design for testability (DFT) to improve the PHM performance level. To solve the problem, a model of TSO for PHM systems is proposed. Firstly, through integrating the characteristics of fault severity and propagation time, and analyzing the test timing and sensitivity, a testability model based on failure evolution mechanism model (FEMM) for PHM systems is built up. This model describes the fault evolutiontest dependency using the fault-symptom parameter matrix and symptom parameter-test matrix. Secondly, a novel method of inherent testability analysis for PHM systems is developed based on the above information. Having completed the analysis, a TSO model, whose objective is to maximize fault trackability and minimize the test cost, is proposed through inherent testability analysis results, and an adaptive simulated annealing genetic algorithm (ASAGA) is introduced to solve the TSO problem. Finally, a case of a centrifugal pump system is used to verify the feasibility and effectiveness of the proposed models and methods. The results show that the proposed technology is important for PHM systems to select and optimize the test set in order to improve their performance level.