摘要
针对航空发动机突发故障,构建了一种基于相似性传播聚类的突发故障诊断方法。首先利用突发故障历史监测数据建立突发故障数据库,通过相似性传播聚类找到数据库中所有突发故障数据的中心,当诊断新采集数据的突发故障类型时,通过相似性传播聚类找到当前新采集数据的中心,经过与突发故障数据库中的数据中心进行匹配判断该新采集数据所对应的突发故障类型。将该突发故障诊断方法应用到发动机转子实验台的突发故障诊断中,仿真和实验结果表明该方法的可行性,并通过与其他方法比较,表明该方法具有诊断时间短和误差小的优点。
Aiming at aero-engine faults,an abrupt fault diagnosis method based on affinity propagation clustering was proposed.Abrupt fault historical monitoring data were used to establish faults database.Through affinity propagation clustering,all the exemplars of abrupt faults in the database were found and the affinity propagation clustering was applied once again to find the exemplar of the new collected data.The fault type was then identified by matching the center with the centers obtained from the faults database.The method was used in the aero-engine abrupt fault diagnosis.The simulation and experiment results show that the method is feasible to diagnose abrupt fault,and compared with other methods,it needs shorter time consuming and produces lower error.
出处
《振动与冲击》
EI
CSCD
北大核心
2014年第1期51-55,共5页
Journal of Vibration and Shock
基金
中国国家自然科学基金(51075330
50975231)
关键词
相似性传播聚类
突发故障诊断
突发故障数据库
中心匹配
航空发动机
affinity propagation clustering
aero-engine abrupt fault diagnosis
abrupt fault database
center matching
aero-engine
