Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (3): 217-221.DOI: 10.3778/j.issn.1002-8331.1608-0253

Previous Articles     Next Articles

Research on fault diagnosis method based on generalized morphological filter and MRSVD

HUANG Gangjing1,2, FAN Yugang1,2, FENG Zao1,2, LIU Yingjie1,2   

  1. 1.Faculty of Information Engineering & Automation, Kunming University of Science and Technology, Kunming 650500, China
    2.Engineering Research Center for Mineral Pipeline Transportation of Yunnan Province, Kunming 650500, China
  • Online:2018-02-01 Published:2018-02-07


黄刚劲1,2,范玉刚1,2,冯  早1,2,刘英杰1,2   

  1. 1.昆明理工大学 信息工程与自动化学院,昆明 650500
    2.云南省矿物管道输送工程技术研究中心,昆明 650500

Abstract: In order to obtain the fault information of rolling bearing under complicated working conditions, a method based on generalized morphological filter and Multi-Resolution Singular Value Decomposition(MRSVD) for fault diagnosis is proposed in this paper. Firstly, the bearing vibration signals are de-noised by using generalized morphological filter. Then the filtered signals are decomposed into a series of component signals by MRSVD. Finally, the detail signals which contained the most valuable fault features are selected by kurtosis criterion. The Hilbert envelope spectrum analysis is applied to the details signals afterwards to obtain the fault information. The experimental results have shown that the proposed method is capable of extracting useful fault features from complex vibration signals, and has offered an approving performance on fault diagnosis of the rolling bearing.

Key words: generalized morphological filter, Multi-Resolution Singular Value Decomposition(MRSVD), Hilbert envelope spectrum analysis, fault diagnosis

摘要: 为了从复杂工况下获取滚动轴承故障信息,提出了一种基于广义形态滤波和多分辨奇异值分解(Multi-Resolution Singular Value Decomposition,MRSVD)相结合的方法。首先利用广义形态学滤波方法对振动信号进行降噪预处理;然后利用MRSVD对降噪后的振动信号进行分解;最后通过峭度准则选取故障特征最丰富的细节信号,并对其进行Hilbert包络谱分析。将提出的方法应用于滚动轴承的故障检测,实验结果表明该方法能清晰地提取故障特征信息。

关键词: 广义形态滤波, 多分辨奇异值分解(MRSVD), Hilbert包络谱分析, 故障诊断