摘要
通过对柴油机运行时的表面振动信号进行分析处理得到由一系列特征参数组成的特征向量,利用模糊C均值聚类方法对特征向量进行模式识别,结果表明该方法可通过特征向量准确地区分不同的柴油机故障模式,模糊C均值聚类方法在柴油机状态监测与故障诊断中有较好的适用性。
The eigenvectors consist of a series of characteristic parameters can be gotten by the analyzing of the surface vibration signals of diesel engine, which represent the different conditions of diesel engine. They can be used to estimate the condition of diesel. The C-means algorithm was used in the clustering of eigenvectots. The results indicates that different fault pattern can be accurately distinguished through eigenvectors by the fuzzy C-means clustering analyzing, and this method can be rightly used to the condition monitoring and fault diagnosis of diesel engine.
出处
《船海工程》
北大核心
2007年第4期56-58,共3页
Ship & Ocean Engineering
关键词
柴油机
C均值算法
聚类分析
故障诊断
diesel engine
C means algorithml Fuzzy clustering analysis
fault diagnosis
