基于粗糙集的故障诊断特征提取

故障的特征提取对于进行准确可靠的诊断非常重要。而实际的故障诊断数据样本的分类边界常常是不确定的,并且故障与征兆之间的关系往往也是不确定的。粗糙集理论是处理模糊和不确定性问题的新的数学工具。论文将粗糙集理论引入到故障诊断特征提取,提出了一种基于粗糙集的故障诊断特征提取方法。并通过两个故障诊断实例对该方法进行了验证。结果表明:在有效地保持故障诊断分类结果的情况下,该方法可以提取出最能反映故障的特征,从而为粗糙集在故障诊断中的深入应用打下了基础。

Fault diagnosis feature subset selection using rough set

Feature subset selection is of prime important for effective fault diagnosis.But the classification boundary of real fault diagnosis data sets is often ambiguous,and the relationships between faults and symptoms are always uncertain.Rough set theory is a novel mathematical tool dealing vagueness and uncertainty.This paper introduces rough set theory and proposes a method for fault diagnosis feature subset selection.By two fault diagnosis examples,this paper validates the method.The results show that this method can efficiently extract the main fault features while the fault classification result is invariable.The research in this paper supplies a basis for further study of applying rough set theory in fault diagnosis.