Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing

Wang Zhixia; Guo Li; Cheng Qianqian

doi:10.16578/j.issn.1004.2539.2018.04.029

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Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing

更新时间：2022-10-20

- Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing
- Vol. 42, Issue 4, Pages: 144-149(2018)
- 作者机构：
  
  1. 陆军工程大学石家庄校区
  2. 陆军北京军代局驻743厂军事代表室
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.04.029
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[1]王志霞,郭利,程茜茜.基于VMD的自适应随机共振在滚动轴承早期故障检测中的应用[J].机械传动,2018,42(04):144-149+163.

Wang Zhixia, Guo Li, Cheng Qianqian. Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing. [J]. 42(4):144-149(2018)
[1]王志霞,郭利,程茜茜.基于VMD的自适应随机共振在滚动轴承早期故障检测中的应用[J].机械传动,2018,42(04):144-149+163. DOI： 10.16578/j.issn.1004.2539.2018.04.029.

Wang Zhixia, Guo Li, Cheng Qianqian. Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing. [J]. 42(4):144-149(2018) DOI： 10.16578/j.issn.1004.2539.2018.04.029.

摘要

针对滚动轴承早期微弱故障难以检测的问题,提出一种基于变分模态分解（Variational mode decompsition,VMD）的自适应随机共振轴承故障检测方法。首先对滚动轴承的振动故障信号进行VMD分解,得到有限个本征模态函数（Intrinsic mode function,IMF）,选取包含故障特征的IMF分量并进行信号重构;将重构信号输入随机共振系统,采用以改进加权峭度为目标函数的量子粒子群算法,优化系统结构参数,得到最佳共振输出,从而实现降噪和增强故障特征的目的,最后通过输出信号的自相关包络谱提取故障特征频率。实测数据的分析结果验证了该方法的有效性和优势。

Abstract

In order to detect the early weak faults of rolling bearings effectively,a method of adaptive stochastic resonance based on variational mode decomposition（ VMD） is proposed. Firstly,the fault vibration signal of rolling bearing VMD is decomposed into finite Intrinsic Mode Functions（ IMF）,IMF components containing fault feature are selected and reconstructed. The reconstructed signal is inputted into the stochastic resonance system,and the system structure parameters are optimized by the quantum particle swarm algorithm with the objective function of the improved weighted kurtosisso as to get the best resonance output,thus realizing the signal de-noising and enhance the fault feature. Finally the fault feature frequency is extracted by the autocorrelation envelope spectrum of output signal. The validity and advantages of the proposed method are proved by the analysis of the measured data.

关键词

变分模态分解自适应随机共振滚动轴承早期故障检测特征频率

Keywords

VMDAdaptive stochastic resonanceRolling bearing early fault detectionCharacteristic frequency

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