模糊神经网络在矿井副井提升系统故障诊断中的应用研究

摘要

The method firstly inputs the fuzzy fault samples of mine hoist to the BP neural network trained in the training data.Then the specific example of hydraulic breaking system was simulated using MATLAB software toolbox.Simulation results shows that fuzzy neural network can filter out redundant information, save time and space, can well predict the fault of mine hoist, and improve the accuracy and reliability of fault diagnosis.%首先把矿井副井提升机故障分类样本进行模糊化，将不可能出现、多余的故障样本剔除，并输入到训练好的BP神经网络里进行数据训练，然后利用MATLAB软件中的工具箱对矿井副井提升系统液压制动系统故障的具体实例进行仿真分析。仿真结果表明，模糊神经网络可以过滤掉多余信息，节省时间和空间，能够很好地对矿井副井提升系统的故障进行预测，提高了故障诊断的准确性和可靠性。