基于神经网络的除尘器故障诊断

针对目前除尘器故障诊断主要基于人工经验判断并结合停机检查，存在科学性与自动化水平不足、诊断效率低等问题，分析了除尘器滤芯破损、清灰失效、滤芯堵塞、卸灰障碍4个主要故障类型，选取粉尘排放浓度、过滤阻力、入口风量、漏风率、耗气量5个诊断参数，建立了除尘器故障诊断的BP和RBF神经网络预测模型。实例分析表明：BP神经网络模型收敛速度快，预测效果理想，可以准确判断除尘器故障类型，对滤芯破损、清灰失效、滤芯堵塞、卸灰故障的平均预测误差分别为0.035%、0.110%、0.118%、0.215%，预测结果优于RBF神经网络。

Malfunction diagnoses of dust collector based on neural network

At present,the malfunction diagnosis of dust collector is mainly based on manual experience judgment and combined with downtime check,which leads to some problems such as low diagnostic efficiency,and lack of science and automation.This paper analyzed four main fault types including filter core damage,ashing failure,filter clogging and ash discharge failure,then selected five diagnostic parameters including dust emission concentration,filtration resistance,inlet air volume,air leakage rate and air consumption to establish BP and RBF neural network prediction model.The example analysis shows that the BP neural network model has fast convergence speed,ideal prediction effect and can accurately judge the fault type of the dust collector.The average prediction model errors of filter core damage,ashing failure,filter clogging and ash discharge failure are 0.035%,0.110%,0.118%,0.215%respectively.The prediction result is better than RBF neural network.