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This paper summarizes and analyzes the typical architecture and evolution of fault prediction and health management technology. We introduce and analyze fault detection, fault diagnosis, and fault prediction functions to explore the architecture of fault prediction and health management systems. This experiment studies the fault prediction method based on solder joint-built in self-test (SJ-BIST). According to the actual monitoring circuit experiments, we adjusted the temperature of the circuit board to 50℃, 100℃, and 150℃ for the circuit board heating treatment for 10 seconds, verified the effectiveness of the SJ-BIST method, and are in line with the law of linear change. The slope of the fitted curve is consistent with the analysis result.

Aiming at the resource optimization problem in the cooperative detection task, the objective function is constructed based on the channel capacity, and the artificial bee colony (ABC) algorithm is improved to realize the joint optimization of the UAV swarm trajectory and radiation power. Firstly, a multiple input multiple output (MIMO) cooperative detection model is constructed. Then, based on the perspective of information theory, the channel capacity of the cooperative detection model is derived and used as the objective function for optimizing the detection resources of UAV swarm. Then, the factors affecting the objective function are sorted out and analyzed one by one, and the constraints are clarified. Aiming at the shortcomings of ABC algorithm, its search strategy and parameter optimization method are improved. A joint optimization process of UAV swarm trajectory and radiated power based on improved ABC algorithm is constructed. Finally, through simulation verification and algorithm comparison, it shows that the algorithm in this paper can improve the perception ability of cooperative detection of UAV swarm.

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