基于Vague集相似度量的汽轮机故障诊断

Fault diagnosis of turbine based on similarity measures between Vague sets

通过对汽轮机故障诊断方法的研究,提出了两种Vague集相似度量在汽轮机故障诊断中的新方法。在汽轮机故障诊断中,Vague集之间的相似度量是评价待检测样本接近系统故障知识的度量,某一相似度量值越大,待检测样本越接近某一故障知识,从而确定故障的类型。通过示例分析表明,两种Vague集相似度量在汽轮机故障诊断中的有效性和合理性。

The two methods of similarity measures between Vague sets were built by the research on fault diagnosis of turbine. The similarity degree between the detecting samples and the knowledge of system fault was evaluated in the fault diagnosis of turbine by the similarity measures between Vague Sets. The bigger the Value of similarity measures was, the better was the similarity degree between the detecting sample and a type of fault knowledge. Then, the type of fault was determined according to the Value of the similarity measure. Example explains the validity and reasonability in the fault diagonsis of turbine by the similarity measure between Vague sets.