基于模糊C均值聚类的柴油机故障诊断  

Fault Diagnosis of Diesel Engine Using Fuzzy C Means Clustering Analysis

作　　者：王志华[1] 赵冬 余永华[1] 

机构地区：[1]武汉理工大学能源与动力工程学院,武汉430063 [2]青岛海事局,山东青岛266011

出　　处：《船海工程》2007年第4期56-58,共3页Ship & Ocean Engineering

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.

关 键 词：柴油机 C均值算法 聚类分析 故障诊断 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]