Bearing Fault Diagnosis Method based on Self-adaptive Stochastic Resonance of Genetic Algorithm and VMD

Zhang Chao; He Yuanyuan

doi:10.16578/j.issn.1004.2539.2018.04.031

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Bearing Fault Diagnosis Method based on Self-adaptive Stochastic Resonance of Genetic Algorithm and VMD

更新时间：2022-10-20

- Bearing Fault Diagnosis Method based on Self-adaptive Stochastic Resonance of Genetic Algorithm and VMD
- Vol. 42, Issue 4, Pages: 156-163(2018)
- 作者机构：
  
  1. 内蒙古科技大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.04.031
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[1]张超,何园园.基于遗传算法的自适应随机共振与VMD分解的轴承故障诊断方法[J].机械传动,2018,42(04):156-163.

Zhang Chao, He Yuanyuan. Bearing Fault Diagnosis Method based on Self-adaptive Stochastic Resonance of Genetic Algorithm and VMD. [J]. 42(4):156-163(2018)
[1]张超,何园园.基于遗传算法的自适应随机共振与VMD分解的轴承故障诊断方法[J].机械传动,2018,42(04):156-163. DOI： 10.16578/j.issn.1004.2539.2018.04.031.

Zhang Chao, He Yuanyuan. Bearing Fault Diagnosis Method based on Self-adaptive Stochastic Resonance of Genetic Algorithm and VMD. [J]. 42(4):156-163(2018) DOI： 10.16578/j.issn.1004.2539.2018.04.031.

摘要

随机共振（Stochastic resonance,SR）在处理实测轴承故障信号时需满足绝热近似条件,即需满足小参数信号（信号幅值、信号频率、噪声强度远小于1）,这一问题极大地制约了对实测振动信号的检测,针对这一现象,提出基于遗传算法的自适应变尺度随机共振与变分模态分解（Variational mode decomposition,VMD）的轴承故障诊断方法。首先,设定合适的压缩比R将实测信号进行压缩,使其满足小参数条件;然后,定义信号的输出信噪比为目标函数,利用遗传算法（Genetic algorithm,GA）对变尺度随机共振的结构参数a和b进行同步优化,选取最优值代入变尺度随机共振中对实测信号进行消噪处理;最后,将降噪信号进行VMD分解,从分解得到的各IMF分量的频谱图中识别轴承故障特征频率。对实验数据分析的结果表明,该方法可有效地提高轴承故障诊断的准确度。

Abstract

The stochastic resonance（ SR） needs to meet the approximative condition when dealing with the measured bearing fault signal,it needs to meet small signal parameters. That is to meet the small parameter signal（ signal amplitude,signal frequency,noise intensity is far less than 1）. The detection of measured vibration signal is greatly restricted by this problem. Aiming at this phenomenon,the self-adaptive re-scaling stochastic resonance based on genetic algorithm（ GA） and the Variational Mode Decomposition（ VMD） is proposed. Firstly,the compression ratio R is set to compress the measured signal appropriately and it needs to meet the small parameter condition. And then,the signal-to-noise ratio of output signal is defined as the objective function. By using the genetic algorithm,the a and b that are structure parameter of re-scaling stochastic resonance are optimized,and the optimal value is selected to drag-in the re-scaling stochastic resonance,the de-noising of measured signal is carried out,Finally,the de-noising signal is decomposed by VMD,from the component spectrum diagram of each IMF,the bearing fault characteristic frequency can be found. Based on the experimental data,the results show that the method can effectively improve the accuracy of bearing fault diagnosis.

关键词

随机共振遗传算法VMD故障诊断

Keywords

Stochastic resonanceGenetic algorithmVMDFault diagnosis

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