吉林大学学报（理学版）2016，Vol.54Issue(3)：609-612,4.DOI:10.13413/j.cnki.jdxblxb.2016.03.36
基于证据理论和支持向量机的风机故障智能诊断
Intelligent Diagnosis of Fan Fault Based on Evidence Theory and Support Vector Machine
摘要
Abstract
In order to improve the accuracy of the fan fault diagnosis,the author presented a new method of fan fault diagnosis which was the combination of evidence theory and support vector machine. Firstly, Wigner-Ville spectrum entropy was extracted from the vibration signal as characteristic of fan fault diagnosis.Secondly,sub-classifier of fan fault diagnosis was established by using different kernel function support vector machines.Finally,the output results of sub-classifier were fused by DS evidence theory,and the performance was simulated and tested.The experimental results show that the proposed method can make full use of all fault information,and the diagnostic results are closer to the expected value,and the diagnosis effect is better than that of other fan fault diagnosis methods.关键词
风机故障/特征提取/证据理论/支持向量机Key words
fan fault/feature extraction/evidence theory/support vector machine分类
信息技术与安全科学引用本文复制引用
李家伟..基于证据理论和支持向量机的风机故障智能诊断[J].吉林大学学报（理学版）,2016,54(3):609-612,4.基金项目
湖北省教育科学“十二五”规划重点项目(批准号:2014A047) （批准号:2014A047）
