【摘要】 提出了一种基于粗糙集理论和模式识别的旋转机械故障诊断方法。该方法包括模式学习和模式匹配 2个过程 ,模式学习用粗糙集方法学习已知故障样本中的标准模式 ,即故障诊断规则 ,模式匹配把待诊断对象和标准模式进行匹配从而进行故障诊断。文中提出的学习方法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已知故障样本 ;在模式匹配时 ,根据条件匹配的程度、规则的置信度和诊断结论阈值获得诊断结论和结论置信度 ,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。更多还原

【Abstract】 A diagnostic method of rotating machinery based on rough sets theory and pattern recognition is proposed. This method includes two processes: one is pattern learning, and the other is pattern recognition. The standard diagnostic rules, i.e. standard patterns, are obtained by rough-set-technique-based learning method from standard fault samples and the diagnostic conclusions are produced by matching diagnostic objects with standard diagnostic rules. The proposed pattern learning method takes into account the reduplicated and conflicting objects in decision table and makes the obtained rules cover all the learning objects. In the pattern recognition, the matching degree of condition attributes of new objects with standard diagnostic rules, the belief degree of those rules and the conclusion threshold are under consideration for conclusion and its belief degree. Consequently, the conclusion based on the proposed method is objective. This method has been used in the diagnosis of rotating machinery and the result is satisfactory.更多还原