基于RBF预测的采煤机故障诊断系统研究

Research on Fault Diagnosis System of Coal Mining Machine Based on RBF Prediction

为提高采煤机运行效率、减小故障发生率,在分析综采工作面采煤机常见故障的基础上,研究基于RBF神经网络的采煤机智能故障诊断方案,建立采煤机故障智能诊断系统,确定RBF神经网络的输入信号和训练样本,并设计故障诊断流程。基于仿真环境对采煤机故障诊断系统进行仿真,并与传统BP神经网络预测故障诊断方案对比,结果表明:基于RBF预测的采煤机故障诊断系统在故障预测实时性、准确性以及稳定性方面有较好的表现。

In order to improve the operation efficiency of coal mining machine and reduce the incidence of fault,the intelligent fault diagnosis scheme of coal mining machine based on RBF neural network is studied on the basis of analyzing the common faults of coal mining machine in comprehensive mining working face.The intelligent fault diagnosis system of coal mining machine is established,the input signal and training samples of RBF neural network are determined,and the fault diagnosis process is designed.Based on the simulation environment,the coal mining machine fault diagnosis system is simulated and compared with the traditional BP neural network prediction fault diagnosis scheme.The results show that the coal mining machine fault diagnosis system based on RBF prediction has better performance in terms of real-time fault prediction,accuracy and stability.