基于小波包和支持向量机的矿井主通风机故障诊断系统的研究  

Study on Fault Diagnosis System of Mine Main Fan Based on Wavelet Packet and Support Vector Machine

作　　者：胡明[1] 郭健鹏[1] 吴静然[1] 崔冉[1] 翟晓东[1] HU Ming;GUO Jian-peng;WU Jing-ran;CUI Ran;ZHAI Xiao-dong(Xuhai College CUMT,Xuzhou 221008,China)

机构地区：[1]中国矿业大学徐海学院

出　　处：《电脑与信息技术》2019年第6期45-47,共3页Computer and Information Technology

基　　金：江苏省青蓝工程资助课题

摘　　要：文章以煤矿通风机的故障诊断为研究背景,针对故障特征信息提取及故障样本缺乏这两个难题,提出了一种基于小波包和支持向量机结合的故障诊断方法。该方法利用小波包分解与重构来提取故障信号的能量特征向量,然后把支持向量机作为故障分类器,实现故障状态的判别,并将其应用在煤矿通风机故障诊断中,对于保障煤矿的安全生产具有重要的理论价值和现实意义。In this paper,the fault diagnosis of mine fan is studied as the background,directing to the problems of extraction of feature information and the lack of samples on the fault diagnosis,this paper presents the fault diagnosis method based on the wavelet packet and support vector machine(SVM).The decomposition and reconstruction of wavelet packet is applied to extracting the fault signal energy eigenvector,the SVM is used as a failure classifier to achieve a state of fault identification,and then,the method is applied to the diagnosis of mine fan which contributes at great extent to protect the safety of coal production on important theoretical value and practical significance.

关 键 词：矿井通风机 小波包分析 支持向量机 故障诊断 

分 类 号：TD411.2[矿业工程—矿山机电] TP391.4[自动化与计算机技术—计算机应用技术]