国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In the process of fault location, the spacecraft is susceptible to the interference of the original power signal, which leads to a poor recognition effect. To solve this problem, a design of a spacecraft online fault detection system based on a multilayer feedforward neural network is proposed. According to the spacecraft online fault detection principle and the Internet of Things technology, the overall architecture of the system is designed, and the hardware and software parts are designed respectively. The hardware part combines the industry standard PC components to design the PXI chassis structure, complete the control of the PXI measurement module, and use the MXI-4 interface tool to achieve remote control to solve the interference signal to the system positioning identification interference. Design the peripheral structure of the EP3C10 chip of FPGA, determine the connection mode of the main and sub adapter pins of the circuit board, use 2 high-speed AD converter differential sampling, and store the sampling results through the FIFO. Through the electronic load board relay control module, control signal blocking performance. A recognition model based on a multi-layer feed-forward neural network is constructed, a recognition threshold is obtained according to deterministic logic inference rules, a specific recognition process is set according to the threshold, and the judgment determines that the parameter is faulty and the system design is completed. Experimental results show that the signal blocking effect of the system is excellent, and the maximum signal amplitude is 0.9 at distances of 2 and 6 m. The detection results of the failure mode are consistent with the ideal results, which can provide equipment support for the stable operation of the spacecraft.