基于模糊聚类分析的汽轮机振动故障自动诊断研究  

Turbine vibration fault diagnosis based on fuzzy clustering analysis

作　　者：王学厚[1] 张楠[1] 董泽[1] 

机构地区：[1]华北电力大学控制科学与工程学院,河北保定071003

出　　处：《华北电力大学学报（自然科学版）》2008年第2期57-60,共4页Journal of North China Electric Power University：Natural Science Edition

摘　　要：通过分析汽轮机振动故障诊断所面临的不确定性等问题,引用模糊理论中隶属度的概念来描述机组振动故障存在的倾向性,采用模糊C均值聚类方法分析机组振动的原因。通过实例检验表明,理论计算与现场检查结果相符,该方法具有良好的实际应用前景,为汽轮机组状态监测及故障自动诊断提供了一种新途径。The concept of subordinate degree of fuzzy theory was introduced to describe the tendency of the steam turbine vibration fault by analyzing the uncertainty in turbine vibration fault diagnosis, and the dynamic fuzzy clustering analysis method was applied to analyze the vibration causes of the unit. The practical examples show that the result calculated by fuzzy clustering is in good agreement with the actual inspections, and the method can be applied to the steam turbine fault diagnosis.

关 键 词：汽轮机振动 故障诊断 模糊C均值聚类 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置]