›› 2009, Vol. 29 ›› Issue (5): 70-73.DOI: 10.3969/j.issn.1006-1355.2009.05.019

• 论文 • Previous Articles     Next Articles

《Fault Diagnosis of Diesel Engine Based on EMD and AR Modes》

LU Jin-ming1,2, WANG Chun-tao2, MA Jie1   

  1. (1. School of Naval Architecture, Ocean and Civil Eng., Shanghai Jiaotong University, Shanghai 200030, China; 2. School of Mechanical and Power Eng., Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212003, China)
  • Received:2009-03-16 Revised:1900-01-01 Online:2009-10-18 Published:2009-10-18
  • Contact: LU Jin-ming

《基于EMD与AR模型的柴油机故障诊断》

陆金铭1,2 王醇涛2 马捷1   

  1. （1.上海交通大学 船舶与海洋工程学院，上海 200030 2.江苏科技大学 机械与动力工程学院， 江苏镇江212003）
  • 通讯作者: 陆金铭

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Abstract:

The vibration signal of a diesel cylinder’s cover is a typical non-stationary signal. Traditional analysis of this signal in the time-frequency domain is not very effective. The interval analysis in time domain is not efficient either to realize real-time diagnosis. And it is also difficult to select the base function in wavelet transform. In this paper, the empirical mode decomposition (EMD) method is used to decompose the vibration signal into a number of intrinsic mode function (IMF) components and then the auto-regressive (AR) model of each IMF component is established. The auto-regressive parameters and the variance of remnant are regarded as the characteristic vectors and served as input parameters of SVM, and the working conditions and defaults of the diesel engine are classified. The results show that the proposed approach can classify working conditions of the diesel engine accurately, and effectively even in the case of small number of samples. And the real-time automation of the diesel engine fault diagnosis can be implemented. In order to classify the faults accurately, it is necessary to select new data as the training samples when the rotating speed of the diesel engine is changed.

Key words: diesel engine, fault diagnosis, empirical mode decomposition (EMD), auto regressive (AR) model, support vector machines (SVM)

摘要：

柴油机气缸盖振动信号是一种典型的非平稳时变信号，用传统的时频分析难以得到满意的效果，用时域区间分析难以实现实时诊断，而小波分析则存在小波基函数选择困难等问题。本文采用经验模式分解EMD方法对振动信号进行分解,得到固有模态函数IMF，对每一个IMF分量分别建立AR模型,以模型的自回归参数和残差的方差作为特征向量,用支持向量机SVM进行分类,判断柴油机的工作状态和故障类型。实验结果分析表明,该方法即使在小样本情况下也能准确有效地诊断柴油机故障,能实现故障的实时自动化诊断。在不同转速时，需选用新转速工况下的数据作为训练样本,以保证分类准确率。

关键词: 柴油机, 故障诊断, 经验模式分解EMD, AR模型, 支持向量机SVM

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