Roller bearing envelope analysis method based on local mean decomposition and kurtogram

ZHANG Kang; CHENG Jun-sheng

doi:10.13224/j.cnki.jasp.2015.12.030

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Roller bearing envelope analysis method based on local mean decomposition and kurtogram

更新时间：2026-07-07

- Roller bearing envelope analysis method based on local mean decomposition and kurtogram
- Vol. 30, Issue 12, Pages: 3043-3050(2015)
- 作者机构：
  
  1. 长沙理工大学能源与动力工程学院,长沙,410076
  2. 湖南大学机械与运载工程学院,长沙,410082
- 作者简介：
- 基金信息：
- DOI：10.13224/j.cnki.jasp.2015.12.030
  CLC：
- Published：2015
- 稿件说明：
移动端阅览
张亢, 程军圣. 基于局部均值分解和峭度图的滚动轴承包络分析方法[J]. 航空动力学报, 2015, 30(12): 3043-3050.

doi: 10.13224/j.cnki.jasp.2015.12.030
张亢, 程军圣. 基于局部均值分解和峭度图的滚动轴承包络分析方法[J]. 航空动力学报, 2015, 30(12): 3043-3050. DOI： 10.13224/j.cnki.jasp.2015.12.030.

doi: 10.13224/j.cnki.jasp.2015.12.030 DOI：

摘要

提出了基于局部均值分解(local mean decomposition

LMD)和峭度图(kurtogram)的滚动轴承包络分析方法.该方法中

原始滚动轴承故障振动信号通过LMD进行自适应的频率成分分离和初步降噪

包络分析中带通滤波器的参数通过峭度图客观地提供

从而提高滚动轴承包络分析的准确度.通过对滚动轴承仿真信号以及实验信号的分析

结果表明:在低信噪比情况下

LMD可以自适应分离出滚动轴承的固有振动成分

峭度图可以自动确定包络分析中带通滤波器的参数

与传统包络分析比较

所提方法能更加清晰准确地提取滚动轴承的故障特征.

Abstract

Roller bearing envelope analysis method based on local mean decomposition (LMD) and kurtogram was proposed. In this method

the original roller bearing fault vibration signal was decomposed into a set of frequency components and preliminary reduced noises adaptively by LMD

and the parameters of band-pass filter in envelope analysis were provided objectively by kurtogram

thus the envelope analysis accuracy for roller bearing can be enhanced. The analytical results from roller bearing simulation signals and experiment signals indicate that the natural vibration components of roller bearing can be separated by LMD adaptively and the parameters of band-pass filter in envelope analysis can be determined by kurtogram automatically in low signal-to-noise ratio. Compared with traditional envelope analysis

the proposed method can extract the fault features of roller bearing more clearly and accurately.

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Related Institution

School of Automatization, Guangdong Polytechnic Normal University

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University

Key Laboratory of Advanced Manufacturing Technology of Beijing, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology

State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University

Key Laboratory of Artificial Intelligence of Yunnan Province， Kunming University of Science and Technology

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