基于IMS聚类算法的柴油发动机故障诊断方法研究

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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。实验研究在一台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。

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	李晓博1 江志农1 张沛2 钱迪2 薛继旭2 郑会2 张进杰1

关键词 ： IMS聚类算法, 柴油发动机, 故障诊断, 特征提取

Abstract：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.

Key words： IMS clustering algorithm diesel engine fault diagnosis feature extraction

收稿日期: 2016-08-30 出版日期: 2018-03-28

引用本文:

李晓博1 江志农1 张沛2 钱迪2 薛继旭2 郑会2 张进杰1. 基于IMS聚类算法的柴油发动机故障诊断方法研究[J]. 振动与冲击, 2018, 37(7): 193-198.
LI Xiao-bo1JiangZhinong1Zhang Pei1 Qian Di1Xue Jixu1 Zheng Hui1 ZHANG Jinjie1. Fault diagnosis approach for diesel engines based on IMS clustering algorithm. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(7): 193-198.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I7/193

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