人工神经网络在离心式通风机故障诊断中的应用  

Application of Artifical Neural Network in Fault Diagnosis for Centrifugal Ventilator

作　　者：荆双喜[1] 绳飘[1] 行志刚[1] 郭燕飞[1] 

机构地区：[1]河南理工大学机械监测与故障诊断研究所,河南焦作454003

出　　处：《煤矿机械》2005年第12期159-161,共3页Coal Mine Machinery

摘　　要：通过对已采集的通风机振动信号分析和整理,提出了应用人工神经网络对其进行故障诊断,以G4-73-11离心式矿用通风机为研究对象,用小波包分解技术提取其振动信号的能量特征作为特征向量,建立了神经网络模型,并应用此网络对一矿用通风机G4-73-11No28D进行故障诊断,结果表明,此网络可作为智能分类器对离心式通风机的常见故障进行识别和诊断。According to the analysis and picking up ventilator vibration signal, the idea that ANN can be used to diagnose the default of ventilator is offered. For G4 - 73 - 11 ventilator, the ANN is established by wavelet packet pick - up eigenvector, when this ANN is applied to diagnosis the G4 - 73 - 11No28D ventilator, the result shows that the working state and default of centrifugal ventilator can be classified and identified by this network.

关 键 词：BP 风机 故障诊断 

分 类 号：TD441[矿业工程—矿山机电] TP183[自动化与计算机技术—控制理论与控制工程]