社会模拟算法优化的随机共振轴承故障诊断研究

余满华; 姜宏; 章翔峰; 李晓巍

doi:10.13568/j.cnki.651094.651316.2020.03.28.0001

您当前的位置：

首页 >

文章列表页 >

社会模拟算法优化的随机共振轴承故障诊断研究

工程技术 | 更新时间：2026-01-21

- 社会模拟算法优化的随机共振轴承故障诊断研究
- 社会模拟算法优化的随机共振轴承故障诊断研究
- 新疆大学学报（自然科学版中英文） 2021年38卷第3期页码：355-360
- 作者机构：
  
  新疆大学机械工程学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金(51765061)
- DOI：10.13568/j.cnki.651094.651316.2020.03.28.0001
  中图分类号： TH133.3
- 纸质出版：2021
- 稿件说明：
移动端阅览
[1]余满华,姜宏,章翔峰,等.社会模拟算法优化的随机共振轴承故障诊断研究[J].新疆大学学报(自然科学版)(中英文),2021,38(03):355-360.
[1]余满华,姜宏,章翔峰,等.社会模拟算法优化的随机共振轴承故障诊断研究[J].新疆大学学报(自然科学版)(中英文),2021,38(03):355-360. DOI： 10.13568/j.cnki.651094.651316.2020.03.28.0001.

DOI：10.13568/j.cnki.651094.651316.2020.03.28.0001.

摘要

针对经典双稳态随机共振在处理调制信号时的局限性和系统参数优化不足的问题

提出了一种社会模拟算法优化的多稳态随机共振轴承故障诊断方法.首先

对振动信号进行Hilbert包络解调和高通滤波;其次

采用频率交换与变尺度将高频特征信号变换到低频区;然后

以信噪比为自适应函数

利用社会模拟算法(Social Mimic Optimization

SMO)优化随机共振系统参数;最后

将变换后的振动信号输入随机共振系统

实现轴承故障诊断.通过实验数据分析证明了所提方法的有效性与优越性.

Abstract

Aiming at the limitation of classical bis-stable stochastic resonance(CSR) in practical application and the shortage of system parameter optimization

a method of bearing fault diagnosis based on multi-stable stochastic resonance optimized by social mimic optimization(SMO) algorithm is proposed. Firstly

the vibration signal is demodulated by Hilbert transform and processed by high pass filter; secondly

the high-frequency characteristic signal is transformed to the low-frequency region by frequency exchange and re-scaling; the signal-to-noise ratio is used as the adaptive function

and the parameters of the stochastic resonance system are optimized by using the social model algorithm; finally

the transformed vibration signal is input into the stochastic resonance system to realize bearing fault diagnosis. The effectiveness and superiority of the proposed method are proved by experimental data analysis.

关键词

Keywords

references

QIN Y,TAO Y,HE Y,et al.Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction[J].Journal of Sound and Vibration,2014,333(26):7386-7400.

LEI Y G,QIAO Z J,XU X F,et al.An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings[J].Mechanical Systems and Signal Processing,2017,94:148-164.

冷永刚,王太勇.二次采样用于随机共振从强噪声中提取弱信号的数值研究[J].物理学报,2003,52(10):2432-2437.LENG Y G,WANG T Y.Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy noise[J].Acta Physica Sinica,2003,52(10):2432-2437.(in Chinese)

张海滨,何清波,孔凡让.基于变参数随机共振和归一化变换的时变信号检测与恢复[J].电子与信息学报,2015,37(9):2124-2131.ZHANG H B,HE Q B,KONG F R.Time-varying signal detection and recovery method based on varying parameter stochastic resonance and normalization transformation[J].Journal of Electronics&Information Technology,2015,37(9):2124-2131.(in Chinese)

林敏,黄咏梅.调制与解调用于随机共振的微弱周期信号检测[J].物理学报,2006,55(7):3277-3282.LIN M,HUANG Y M.Modulation and demodulation for detection weak periodic signal of stochastic resonance[J].Acta Physica Sinica,2006,55(7):3277-3282.(in Chinese)

TAN J Y,CHEN X F,WANG J Y,et al.Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis[J].Mechanical Systems and Signal Processing,2009,23(1):811-822.

刘进军,冷永刚,赖志慧,等.基于频域信息交换的随机共振研究[J].物理学报,2016,65(22):193-206.LIU J J,LENG Y G,LAI Z H,et al.Stochastic resonance based on frequency information exchange[J].Acta Physica Sinica,2016,65(22):193-206.(in Chinese)

LIU J J,LENG Y G,LAI Z H,et al.Multi-frequency signal detection based on frequency exchange and re-scaling stochastic resonance and its application to weak fault diagnosis[J].Sensors,2018,18(5):1325-1343.

QIAO Z J,LEI Y G,LI N P.Applications of stochastic resonance to machinery fault detection:A review and tutorial[J].Mechanical Systems and Signal Processing,2019,122:502-536.

马川,李宏坤,赵利华,等.运用小波包峭度包络的滚动轴承故障诊断[J].振动测试与诊断,2011,31(6):720-723+812.MA C,LI H K,ZHAO L H.Rolling bearing fault diagnosis using wavelet packet kurtosis envelope[J].Journal of Vibration,Measurement&Diagnosis,2011,31(6):720-723+812.(in Chinese)

冯毅,陆宝春,张登峰.基于多稳态随机共振的轴承微弱故障信号检测[J].振动测试与诊断,2016,36(6):1168-1174+1240-1241.FENG Y,LU B C,ZHANG D F.Bearing weak fault signal detection based on adaptive multi-stable stochastic resonance[J].Journal of Vibration,Measurement&Diagnosis,2016,36(6):1168-1174+1240+1241.(in Chinese)

张仲海,王多,王太勇,等.采用粒子群算法的自适应变步长随机共振研究[J].振动与冲击,2013,32(19):125-130+152.ZHANG Z H,WANG D,WANG T Y,et al.Self-adaptive step-changed stochastic resonance using particle swarm optimization[J].Journal of Vibration And Shock,2013,32(19):125-130+152.(in Chinese)

LIU X L,LIU H G,YANG J H,et al.Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system[J].Mechanical Systems and Signal Processing,2017,96:58-76.

HE B,HUANG Y,WANG D Y,et al.A parameter-adaptive stochastic resonance based on whale optimization algorithm for weak signal detection for rotating machinery[J].Measurement,2019,136:658-667.

孔德阳,彭华,马金全.基于人工鱼群算法的自适应随机共振方法研究[J].电子学报,2017,45(8):1864-1872.KONG D Y,PENG H,MA J Q.Adaptive stochastic resonance method based on artificial-fish swarm optimization[J].Acta Electronica Sinica,2017,45(8):1864-1872.(in Chinese)

BALOCHIAN S,BALOOCHIAN H.Social mimic optimization algorithm and engineering applications[J].Expert Systems with Applications,2019,134:178-191.

LI J M,CHEN X F,HE Z J.Multi-stable stochastic resonance and its application research on mechanical fault diagnosis[J].Journal of Sound and Vibration,2013,332(22):5999-6015.

浏览量

215

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据