一种柴油机燃油系统故障诊断的新方法  

A New Method of Diesel Engine Fuel System Fault Diagnosis

作　　者：邓国红[1] 曹龙汉[2] 刘进 李建勇[2] 蒲红梅[2] 

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400030 [2]重庆通信学院全军通信电源研究中心,重庆400035 [3]重庆川仪总厂有限公司技术中心,重庆400700

出　　处：《重庆大学学报（自然科学版）》2004年第10期8-13,共6页Journal of Chongqing University

基　　金：重庆市自然科学基金研究项目(2004-2718)

摘　　要：提出一种基于小波变换和模糊C-均值聚类分析的柴油机燃油系统故障诊断方法。对测得的柴油机高压油管压力波形进行小波变换,提取了反映故障状态的5个特征参数。给出了模糊C-均值聚类算法的原理及一般步骤,指出用划分系数、划分熵和分离系数可以评价模糊聚类的有效性。利用模糊C-均值聚类算法建立标准故障特征模式,通过待识样本与标准模式的贴近度计算和比较,可以完成故障模式的模式识别。该方法应用于柴油机燃油系统的典型故障诊断中,测试表明采用小波分析和模糊C-均值聚类分析后,提高了判别故障的准确性。A method of diesel engine fuel system fault diagnosis based on wavelet transform and fuzzy C-means clustering is presented. Five characteristic parameters of reflecting fault state are distilled with wavelet transform of pressure wave of high-pressure oil pipe of diesel. The theory and generic approach of fuzzy C-means clustering algorithm (FCM) is given, and the validity of evaluating fuzzy clustering making use of partition coefficient, partition entropy and parting coefficient is pointed out. Identification of fault mode can be completed utilizing standard fault character modes established by FCM algorithm, and calculating and comparing the similarity degree between this standard mode and sample. The arithmetic is applied to all kinds of typical faults diagnose in the diesel engine fuel system. Measuring results indicate that the precision of fault diagnosis is increased with the analysis of wavelet and FCM.

关 键 词：柴油机 故障诊断 小波变换 模糊C—均值聚类 

分 类 号：TK418[动力工程及工程热物理—动力机械及工程] TP182[自动化与计算机技术—控制理论与控制工程]