摘要
将IMS聚类算法引入柴油发动机故障诊断中,首先对柴油机各工况振动信号进行特征提取,之后对提取的信号特征进行选择;最后建立IMS聚类算法模型,将提取到的特征量作为该模型的输入参数,实现发动机故障的智能诊断。试验研究在一台V6涡轮增压柴油发动机上进行,以获取训练和验证IMS聚类算法模型的数据。经过数据验证,该模型对于故障的判断全部正确。当前的研究为柴油发动机故障的诊断提出了一个新的检测途径。
Here,the IMS clustering algorithm was used to study fault diagnosis of diesel engines.Firstly,vibration signal features of a diesel engine were extracted,these features were selected.Finally,an IMS clustering algorithm model was established,the features extracted from fault data were taken as the model’s input parameters to realize diesel engine faults’intelligent diagnosis.Test study was performed on a V6 turbocharged diesel engine to get data for both training purposes and verifying the IMS clustering algorithm model.Through verification of data,it was shown that judgements of the model for faults are correct.The study provided a new detecting way for fault diagnosis of diesel engines.
作者
李晓博
江志农
张沛
钱迪
薛继旭
郑会
张进杰
LI Xiaobo;JIANG Zhinong;ZHANG Pei;QIAN Di;XUE Jixu;ZHENG Hui;ZHANG Jinjie(Diagnosis and Self-Recovery Engineering Research Center,Beijing University of Chemical Technology,Beijing 100029,China;Petro China Beijing Natural Gas Pipeline Company Limited,Beijing 100101,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2018年第7期193-198,246,共7页
Journal of Vibration and Shock
基金
863项目(2014AA041806)
中央高校基本科研业务费专项资金资助(ZY1617)
双一流建设专项经费资助(ZD1601)
关键词
归纳监视系统
聚类算法
柴油发动机
故障诊断
特征提取
inductive monitoring systems(IMS)
clustering algorithm
diesel engine
fault diagnosis
feature extraction
作者简介
李晓博男,硕士,1992年生;通信作者江志农男,教授,1967年生
