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针对矿井提升机制动器经常出现故障、耦合信号导致故障诊断相对复杂的问题,采用遗传算法优化BP神经网络的方法,通过调查和研究矿井提升机制动器的故障类型,收集相关数据,根据GA-BP神经网络确定网络的输入量和输出量,对矿井提升机制动器进行故障诊断.利用Matlab进行遗传算法优化的BP神经网络故障诊断的仿真分析.研究结果表明,诊断误差较小,输出向量与实际的故障结果一致,所以将遗传算法优化BP神经网络应用到矿井提升机制动器的故障诊断中是有效且可行.
Aiming at the problems that the brakes of mine hoist often appear faults and the coupling signals lead to the relatively complicated fault diagnosis, a genetic algorithm is used to optimize the BP neural network. By investigating and studying the fault types of mine hoist brakes and collecting relevant data, Network to determine the input and output of the network, mine mine brakes fault diagnosis.Using Matlab to optimize BP neural network fault diagnosis simulation analysis.The results show that the diagnostic error is small, the output vector and the actual fault The results are consistent, so it is effective and feasible to apply genetic algorithm to optimize BP neural network to fault diagnosis of mine hoist brakes.