基于改进LMD和IED-SampEn的齿轮故障特征提取方法

doi:10.3901/JME.2016.05.169

机械工程学报 ›› 2016, Vol. 52 ›› Issue (5): 169-174.doi: 10.3901/JME.2016.05.169

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基于改进LMD和IED-SampEn的齿轮故障特征提取方法

孟宗^{1, 2}, 王亚超¹, 胡猛¹

1. 燕山大学河北省测试计量技术及仪器重点实验室秦皇岛 066004;
2. 燕山大学国家冷轧板带装备及工艺工程技术研究中心秦皇岛 066004

出版日期:2016-03-05 发布日期:2016-03-05
作者简介:孟宗 (通信作者),男,1977年出生,博士,教授。主要研究方向为信号分析与处理、旋转机械故障诊断。E-mail：mzysu@ysu.edu.cn;王亚超,女,1988年出生。主要研究方向为振动信号分析与处理、旋转机械故障特征提取。E-mail：yachaowang0912@126.com;胡猛,男,1990年出生。主要研究方向为信号时频分析、机械状态监测与故障诊断。E-mail：1936794989@qq.com
基金资助:
* 国家自然科学基金(51575472, 51105323)和河北省自然科学基金 (E2015203356)资助项目

Fault Feature Extraction Method of Gear Based on Improved Local Mean Decomposition and Instantaneous Energy Distribution-sample Entropy

MENG Zong^{1, 2}, WANG Yachao¹, HU Meng¹

1. Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao 066004;
2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Yanshan University, Qinhuangdao 066004

Online:2016-03-05 Published:2016-03-05

摘要/Abstract

摘要： 提出基于改进的局部均值分解(Local mean decomposition, LMD)和瞬时能量分布(Instantaneous energy distribution, IED)-样本熵(Sample entropy, SampEn)的齿轮故障特征提取方法。针对LMD存在的端点效应问题,提出最大相似系数法改进的LMD方法,该方法通过在信号内部寻找与两端指定波段相似系数最大的波段,来实现端点效应的改善。进行仿真验证,结果表明该方法能有效改善LMD的端点效应问题。采用改进的LMD方法分解信号得到瞬时幅值函数,由此可以获得信号的瞬时能量分布,将其作为样本熵输入获得IED-SampEn,通过试验研究并与PF-SampEn进行对比,结果表明IED-SampEn能够合理地、有效地反应齿轮的故障状态,作为齿轮振动信号的特征矢量具有典型性,可以作为一种有效的故障特征。

关键词: 齿轮, 改进局部均值分解, 故障特征提取, 瞬时能量分布, 样本熵

Abstract: A method of gear fault feature extraction based on an improved local mean decomposition (LMD) and instantaneous energy distribution (IED) - sample entropy (SampEn) is proposed. Aiming at end effects of LMD, the maximum similarity coefficient improved LMD method is put forward. The method achieves the improvement of end effect by looking for bands that have the biggest similarity coefficient to the specified bands at both ends in the internal signal. The simulation results show that this method can effectively improve the end effect of LMD. Using the improved LMD to decompose the signal can get instantaneous amplitude functions, from that, instantaneous energy distributions of the signal as sample entropy input of IED-SampEn can be obtained. Through the experimental study and compared with PF-SampEn, the results show that IED-SampEn can reasonably and effectively response gear fault state, it is typical as the feature vector of gear vibration signal and can be used as an effective fault feature.

Key words: fault feature extraction, gear, improved local mean decomposition, instantaneous energy distribution, sample entropy

中图分类号:

TN911

孟宗, 王亚超, 胡猛. 基于改进LMD和IED-SampEn的齿轮故障特征提取方法[J]. 机械工程学报, 2016, 52(5): 169-174.

MENG Zong, WANG Yachao, HU Meng. Fault Feature Extraction Method of Gear Based on Improved Local Mean Decomposition and Instantaneous Energy Distribution-sample Entropy[J]. Journal of Mechanical Engineering, 2016, 52(5): 169-174.

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基于改进LMD和IED-SampEn的齿轮故障特征提取方法

Fault Feature Extraction Method of Gear Based on Improved Local Mean Decomposition and Instantaneous Energy Distribution-sample Entropy

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