摘要
提出了一种基于模糊C均值聚类和粗糙集理论的旋转机械故障诊断方法.该方法包括粗糙集规则学习和诊断规则匹配两个过程.其中,学习过程考虑了样本中的重复对象和冲突对象,使获得的诊断规则能够覆盖所有的学习样本,并得到规则强度;在诊断规则匹配时,根据规则中条件属性的属性重要性、条件属性匹配的程度、规则强度以及诊断结论阈值得到诊断结论,从而使得到的结论更客观.最后,通过实验验证了该方法的有效性.
A diagnostic method on rotating machinery based on fuzzy C-means clustering and rough set theory is proposed in this paper, which includes two processes: one is rough-set-technique-based diagnostic rules requisition and the other is diagnosis of new objects based on the rules obtained. The diagnostic rule requisition process takes into account the duplicated and conflicting objects in decision table and makes the rules obtained covering all the learning objects, and obtains corresponding rules strength. In the diagnostic process, the significance of condition attributes of rales, the matching degree of condition attributes of objects with diagnostic rales, the strength of those rules and the conclusion threshold is considered to give diagnosis conclusion and belief degree of conclusion. So the conclusion based on the proposed method is objective. In the end of this paper, this method is employed to diagnose the faults of rotating machinery and the result is proved to be effective.
出处
《信息与控制》
CSCD
北大核心
2004年第3期355-360,共6页
Information and Control
基金
十五国家科技攻关计划重点资助项目(2001BA204B05-KHKZ0009)
关键词
旋转机械
故障诊断
粗糙集
模糊C均值聚类
属性重要性
rotating machinery
fault diagnosis
rough set theory
fuzzy C-means clustering
significance of condition attributes
