知识与财富的链接
通过对已采集的矿井通风机振动信号的处理,提出应用小波包-人工神经网络对其进行故障诊断与监测。以G4-73-11No25D离心式通风机为研究对象,利用小波包提取振动信号的能量特征作为特征向量,并利用L-M算法对BP网络进行改进,建立了神经网络模型。经实际验证,该方法能够准确、快速地对通风机的故障进行诊断和监测。
According to process the collection of the mine ventilator vibration signal.The idea that wavelet packets-artifical neural network can be used to diagnose and predict the default of mine ventilator is offered.For G4-73-11No25D ventilator,the energy feature of extraction of vibration signal using wavelet packet is a feature vector,and BP network is improved by L-M algorithm,the ANN is established.Examined by practice,the method can accurately and quickly diagnose and predict to the ventilator fault.
