• CN:11-2187/TH
  • ISSN:0577-6686

›› 2014, Vol. 50 ›› Issue (7): 70-77.

• 论文 • 上一篇    下一篇

基于集合经验模式分解能量分布与灰色相似关联度的齿轮故障诊断

张文斌; 苏艳萍;普亚松;郭德伟;滕瑞静   

  1. 红河学院工学院;浙江大学机械工程学系
  • 出版日期:2014-04-05 发布日期:2014-04-05

Gear Fault Diagnosis Method Using Ensemble Empirical Mode Decomposition Energy Distribution and Grey Similar Incidence

ZHANG Wenbin, SU Yanping, PU Yasong, GUO Dewei, TENG Ruijing   

  1. College of Engineering, Honghe University; Department of Mechanical Engineering, Zhejiang University
  • Online:2014-04-05 Published:2014-04-05

摘要: 针对齿轮发生故障时,其不同频带能量分布与其故障状态间存在一定的映射关系,提出一种基于集合经验模式分解与灰色相似关联度相结合的故障诊断方法。引入循环统计学的思想对传统形态滤波方法进行改进,定义顺序形态滤波器,并结合实际选用最简单的直线结构元素,对实测齿轮原始加速度振动信号进行顺序形态滤波降噪预处理。采用集合经验模式分解方法将降噪后的齿轮非平稳加速度振动信号分解为有限个平稳的本征模态函数,从中选取包含故障主要信息的前几个本征模态函数分量并计算其能量分布。由于灰色相似关联度分析对小样本模式识别具有良好的分类效果,以能量分布为元素构造特征矢量,通过计算不同振动信号的灰色相似关联度来判断齿轮的工作状态和故障类型。实例分析结果表明,提出的方法能够有效地应用于齿轮系统的故障诊断。

关键词: 集合经验模式分解;能量分布;灰色相似关联度;顺序形态滤波;故障诊断;齿轮

Abstract: Focusing on some correspondence between energy distribution of different frequency bands and fault types when gear fault occurs, a new comprehensive fault diagnosis method is proposed based on ensemble empirical mode decomposition and grey similar incidence. The idea of circle statistics is introduced to improve the shortcoming of traditional morphological filter; and the rank-order morphological filter is defined. The line structure element is selected for rank-order morphological filter to denoise the original acceleration vibration signal. Denoised vibration signals are decomposed into a finite number of stationary intrinsic mode functions and some containing the most dominant fault information are calculated the energy distribution. Due to the grey similar incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gear vibration signals is calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

Key words: ensemble empirical mode decomposition;energy distribution;grey similar incidence;rank-order morphological filtering;fault diagnosis;gear

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