Research on Fault Diagnosis of Rolling Bearing based on Synchrosqueezing Extracting Transform

Qi Liu; Yanxue Wang

doi:10.16578/j.issn.1004.2539.2021.01.020

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Research on Fault Diagnosis of Rolling Bearing based on Synchrosqueezing Extracting Transform

Test·Analysis | 更新时间：2022-11-10

- Research on Fault Diagnosis of Rolling Bearing based on Synchrosqueezing Extracting Transform
- Journal of Mechanical Transmission Vol. 45, Issue 1, Pages: 123-128(2021)
- 作者机构：
  
  1.北京建筑大学机电与车辆工程学院，北京 100044
  2.北京建筑大学城市轨道交通车辆服役性能保障重点实验室，北京 100044
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2021.01.020
  CLC：
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刘奇,王衍学.基于同步挤压提取变换的滚动轴承故障诊断研究[J].机械传动,2021,45(01):123-128.

Liu Qi,Wang Yanxue.Research on Fault Diagnosis of Rolling Bearing based on Synchrosqueezing Extracting Transform[J].Journal of Mechanical Transmission,2021,45(01):123-128.
刘奇,王衍学.基于同步挤压提取变换的滚动轴承故障诊断研究[J].机械传动,2021,45(01):123-128. DOI： 10.16578/j.issn.1004.2539.2021.01.020.

Liu Qi,Wang Yanxue.Research on Fault Diagnosis of Rolling Bearing based on Synchrosqueezing Extracting Transform[J].Journal of Mechanical Transmission,2021,45(01):123-128. DOI： 10.16578/j.issn.1004.2539.2021.01.020.

摘要

滚动轴承是大型机械设备的重要部件，起着非常重要的作用。当轴承发生故障时，如不及时修复或更换，将严重影响设备的寿命。时频分析方法是一种非常有效的故障特征提取工具，已得到广泛的应用；同时，时频分布的能量聚集性影响故障特征提取效果，因此，一种能量更加集中的时频分析方法对机械信号处理与故障诊断起着至关重要的作用。提出了一种全新的时频域特征提取方法——同步挤压提取变换。该方法主要包含两个步骤：首先，使用同步挤压变换将信号大部分能量聚集到多个小频带范围内，实现信号能量的初步聚集；然后，在同步挤压变换结果中引入一个频率提取算子，该算子可以提取出每个小频带内与信号时变特征最相关的信息并将其保留，从而实现信号能量的再次聚集。仿真信号的分析验证了该方法的可行性。通过对实际轴承信号的分析发现，与先前的时频分析方法相比，该方法效果更佳。

Abstract

Rolling bearings are important components of large machinery and play a very important role. When the bearing fails， if not repaired or replaced them in time， it will seriously affect the life of the equipment. Time-frequency analysis method is a very effective fault feature extraction tool， which has been widely used. Simultaneously， the energy concentration of time-frequency representation affects the effect of fault feature extraction， so a more concentrated time-frequency analysis method plays a vital role in mechanical signal processing and fault diagnosis. A novel time-frequency domain feature extraction method， synchrosqueezing extracting transform is proposed. This method mainly includes two steps， firstly， the majority of energy of the signal is collected into multiple small frequency bands by using a synchrosqueezing transform， which achieves the initial concentration and reduce the energy loss of the next step. Then， a frequency extracting operator is introduced into the results of the synchrosqueezing transform. This operator can extract the information that is most relevant to the time-varying characteristics of the signal in each small frequency band and retain it， which achieves the concentration again. The analysis of simulation signals verified the feasibility of the method. Finally， by analyzing the actual bearing signals， it is found that the proposed method is more effective than the previous time-frequency analysis methods.

关键词

同步挤压变换同步挤压提取变换滚动轴承故障诊断

Keywords

Synchrosqueezing transformSynchrosqueezing extracting transformRolling bearingFault diagnosis

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