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A new approach to select an optimal set of test points is proposed. The described method uses fault-wise table and multiobjective genetic algorithm to find the optimal set of test points. First, the fault-wise table is constructed whose entries are measurements associated with faults and test points. The problem of optimal test points selection is transformed to the selection of the columns that isolate the rows of the table. Then, four objectives are described according to practical test requirements. The multi-objective genetic algorithm is explained. Finally, the presented approach is illustrated by a practical example. The results indicate that the proposed method efficiently and accurately finds the optimal set of test points and is practical for large scale systems.

Prognostics and health management (PHM) has an important part in aerospace systems. Information sensing and testing are the bases of PHM, and design for testability (DFT) developed concurrently with system design is considered a fundamental way to improve PHM performance. The traditional DFT, which is only based on the requirements of fault detection and isolation, is not suitable for sensor design and optimization for PHM. Aiming to solve this problem, the intrinsic requirements of PHM for testability are firstly analyzed qualitatively and the corresponding testability indexes are defined quantitatively. Then, a sensor selection/optimization process for PHM is presented. Fault detection uncertainty is also analyzed systematically from the view of fault attributes, sensor attributes and fault-sensor matching attributes, respectively. Based on the requirements and process, the object and constraint models of sensor optimization selection problem are studied in great detail. For aerospace system health management, a sensor optimization selection model is constructed that treats sensor total cost as the objective function and the proposed testability indexes under uncertainty test as constraint conditions. Due to the NP-hard property of the model, a generic algorithm (GA) is introduced to obtain the optimal solution. The application examples show that the proposed model and algorithm are effective and feasible.