摘要
矿井风机是矿下作业的重要机械之一,针对矿井风机振动故障诊断中故障征兆的使用问题,基于FCM聚类分析和条件熵相结合的方法提出了一种新的属性约简方法,首先按照特征频率实现对风机故障的聚类,接着结合最大隶属原则和条件熵完成对属性的约简并最终为专家系统提供有效的诊断规则库,从而能更好的实现故障的诊断。
Mine ventilator is one of the important mechanical device in the underground work, A method based on FCM and conditional entropy is proposed to realize attribute reduction.It can provide an effective diagnosis expert system rule base, ac-cording to characteristic frequency the clustering of the fan is realized, and then combining with the maximum membership principle and conditional entropy , the attribution reduction is complished , and the valid diagnosis rule base is provided , which can better realize fault diagnosis.
出处
《机械研究与应用》
2013年第6期43-45,52,共4页
Mechanical Research & Application
关键词
特征频率
FCM
条件熵
属性约简
最大隶属规则
characteristic frequency
FCM
conditional entropy
attribute reduction
maximum membership principle
