Test configuration is an important part in the process of testability design. Most of the existing test configuration methods are based on multi-signal flow diagram model and adopt genetic algorithm or particle swarm optimization (pso). However, there are many problems in this solution. First of all, the multi-signal flow diagram model has poor ability to express uncertain information, low model accuracy, large deviation in the calculation of testability indicators, and the model does not have the ability to learn and update. Secondly, the efficiency of genetic algorithm is low and the computation time is long, while the binary discrete particle swarm optimization algorithm is easy to fall into the local optimal. To solve the above two problems, a test configuration method based on the hybrid algorithm of genetic -binary discrete particle swarm optimization is proposed. This method can combine the global search ability of genetic algorithm with the optimal speed of binary particle swarm optimization, and use the bayesian network model to calculate more accurate testability indexes. It is proved that the algorithm can make full use of the high-precision information provided by the model, and the calculation speed is fast.And it is not easy to fall into the local optimal solution.

Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.

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The optimal test point placement problem in existing research results is mainly limited to the qualitative study of whether faults can be diagnosed without considering the difficulty of diagnosing faults. We proposed an optimal test point placement approach based on fault diagnosability quantitative evaluation to solve the above problem. First, the fault diagnosability is quantitatively evaluated based on the maximum mean discrepancy (MMD). Then, the problem of optimal test point placement is considered a multi objective optimization problem. The optimal test point set is solved using the multi objective sparrow search algorithm (MOSSA) based on the fault diagnosability quantitative evaluation results, considering limitations on the test point number, reliability, and cost. Finally, the proposed approach is used to optimize the placement of test points in the switching power supply system. The simulation results show that only three test points need to be placed to make the system meet the fault diagnosability requirements. Two test point placement schemes are obtained, which can be selected according to different practical requirements. The experimental results illustrate that the proposed approach can optimize the system test point placement while ensuring good fault diagnosability.INDEX TERMS Fault diagnosability, quantitative evaluation, maximum mean discrepancy, test point placement, sparrow search algorithm