EEMD—模糊聚类在共轨系统故障诊断上的应用研究
Research on the Application of EEMD Fuzzy Clustering in Fault Diagnosis of Common Rail Systems
李良钰 1苏铁熊 1马富康 2蒲瑜2
作者信息
- 1. 中北大学机电工程学院,山西 太原 030051
- 2. 中北大学能源动力工程学院,山西 太原 030051
- 折叠
摘要
高压共轨柴油机作为一种往复式运行的机械,其故障的发生是一个具有模糊性的渐变过程.针对高压共轨柴油机供油系统故障程度诊断中出现的特征值识别困难,分类界限模糊等问题,提出了一种基于EEMD(集合经验模态分解)—模糊聚类的故障诊断方法.通过EEMD将供油系统轨压信号分解为一系列的IMF(固有模态函数),利用过零率曲线确定的特征提取准则并提取本征模态函数中的特征值,建立模糊聚类模型进行故障程度的诊断.在此基础上通过台架实验获得轨压信号,提取了相关特征值进行识别,分析了诊断结果,验证了该方法的正确性.
Abstract
As a kind of reciprocating running machinery,the occurrence of faults in high-pressure common rail diesel engine is a grad-ual process with ambiguity.For the problems of difficult identification of eigenvalues and fuzzy classification boundaries in the diagno-sis of the fault degree of high-pressure common rail diesel engine oil supply system,a fault diagnosis method based on EEMD(ensem-ble empirical modal decomposition)-fuzzy clustering is proposed in this paper.By decomposing the oil supply system rail pressure signal into a series of IMFs(intrinsic modal functions)through EEMD,the eigenvalues in the intrinsic modal functions are extracted using the feature extraction criterion determined by the over-zero rate curve,and a fuzzy clustering model is established to diagnose the fault degree.On this basis,the rail pressure signals are obtained by bench experiments,the relevant eigenvalues are extracted for identifica-tion,the diagnostic results are analyzed,and the correctness of the method is verified.
关键词
高压共轨/故障诊断/集合经验模态分解/模糊聚类Key words
High-Pressure Common Rail/Fault Diagnosis/Ensemble Empirical Mode Decomposition/Fuzzy Clustering引用本文复制引用
出版年
2024
NETL
NSTL
万方数据