摘要
为了提高风机故障的诊断精度,提出一种证据理论和支持向量机相融合的风机故障识别方法.首先从振动信号中提取Wigner-Ville谱熵作为风机故障诊断特征;然后采用不同核函数支持向量机进行训练,建立风机故障诊断的子分类器;最后采用DS证据理论对子分类器的输出结果进行融合,并对其性能进行仿真测试.实验结果表明,该方法可以充分利用全部故障信息,诊断结果更接近期望值,诊断效果优于其他风机故障诊断方法.
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.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2016年第3期609-612,共4页
Journal of Jilin University:Science Edition
基金
湖北省教育科学"十二五"规划重点项目(批准号:2014A047)
关键词
风机故障
特征提取
证据理论
支持向量机
fan fault
feature extraction
evidence theory
support vector machine
