计算机科学 ›› 2018, Vol. 45 ›› Issue (9): 146-151.doi: 10.11896/j.issn.1002-137X.2018.09.023

• 网络与通信 • 上一篇    下一篇

级联三稳态随机共振的特性研究及应用

张刚, 高俊鹏, 李红威   

  1. 重庆邮电大学信号与信息处理重庆市重点实验室 重庆400065
  • 收稿日期:2017-08-09 出版日期:2018-09-20 发布日期:2018-10-10
  • 通讯作者: 高俊鹏(1988-),男,硕士,主要研究方向为微弱信号检测,E-mail:752343533@qq.com
  • 作者简介:张 刚(1976-),男,博士,副教授,主要研究方向为混沌保密通信、微弱信号检测;李红威(1989-),男,硕士,主要研究方向为微弱信号检测,E-mail:752343533@qq.com。
  • 基金资助:
    本文受国家自然科学基金项目(61671095,61371164,61275099),重庆市教育委员会科研项目(KJ1600427,KJ1600429)资助。

Research on Stochastic Resonance Characteristics of Cascaded Three-steady-state and Its Application

ZHANG Gang, GAO Jun-peng, LI Hong-wei   

  1. Chongqing Key Laboratory of Signal and Information Processing,Chongqing University of Posts and
    Telecommunications,Chongqing 400065,China
  • Received:2017-08-09 Online:2018-09-20 Published:2018-10-10

摘要: 针对强噪声环境下存在的微弱信号检测困难的问题,以级联三稳态随机共振系统为研究对象,利用信噪比增益和特征频率的频谱峰值作为判断指标,对三稳态随机共振的特性进行分析。仿真结果验证了通过调节级联三稳态随机共振系统的相关参数,能够获得比单级三稳态随机共振系统更好的随机共振输出特性。此外,针对弱信号在实际的齿轮故障诊断中难以提取的问题,提出级联三稳态随机共振齿轮故障的诊断方法。结果表明,该方法可以有效提取齿轮故障的微弱特征,进而实现齿轮的早期故障诊断,具有广泛的工程应用前景。

关键词: 参数调节, 齿轮, 故障诊断, 级联三稳态随机共振系统, 微弱信号检测

Abstract: In order to solve the problem of weak signal detection difficulties in strong noise environment,using SNR gain and the spectral height of characteristic frequency as the measurement indexes,this paper studied the cascaded tri-stable stochastic resonance system and analyzed its characteristics.The simulation results show that the cascaded tri-stable stochastic resonance system can achieve better output than single-stage tri-stable resonance system through tu-ning the parameters.In addition,in order to solve the problem that the weak signal in the actual gear fault diagnosis is difficult to extract,this paper proposed a gear fault diagnosis method by using cascaded tri-stable stochastic resonance system.The results show that this method can effectively extract the weak characteristics of gear fault,and realize the early gear fault diagnosis.Therefore,it has a wide range of engineering application prospects.

Key words: Cascaded tri-stable stochastic resonance system, Fault diagnosis, Gear, Parameter tuning, Weak signal detection

中图分类号: 

  • TN911.23
[1]BENZI R,SUTERA A,VULPIANI A.The mechanism of stochastic resonance[J].Journal of Physics A General Physics,2015,14(11):L453.
[2]GAMMAITONI L,HÄNGGI P,JUNG P,et al.Stochastic resonance[J].Review of Modern Physics,1998,70(1):45-105.
[3]LENG Y G.High frequency resonance mechanism of bistable
parametric tuning[J].Acta Physica Sinica,2011,60(2):1-7.(in Chinese)
冷永刚.双稳态调参高频共振机理[J].物理学报,2011,60(2):1-7.
[4]LENG Y G,WANG T Y,GUO Y,et al.Stochastic resonance behaviors of bistable systems connected in series[J].Acta Physica Sinica,2005,54(3):1118-1125.
[5]QU Y,WANG F Z,SUN J J.Enhancement of stochastic resonance in cascades bistable systems[J].Chinese Science:Physics,Mechanics,Astronomy,2011,41(10):1190-1197.(in Chinese)
曲媛,王辅忠,孙静静.级联双稳系统随机共振的加强[J].中国科学:物理学力学天文学,2011,41(10):1190-1197.
[6]LAI Z H,LENG Y G,FAN S B.Study on stochastic resonance of cascaded bistable Duffing systems[J].Acta phys Sinica,2013,62(7):61-69.(in Chinese)
赖志慧,冷永刚,范胜波.级联双稳Duffing系统的随机共振研究[J].物理学报,2013,62(7):61-69.
[7]ZHOU Y F,WANG H J,ZUO Y B.Acquisition of characteristic information of weak faults based on cascaded stochastic resonance system[J].Journal of Beijing Information Science & Technology University,2016,31(3):32-36.(in Chinese)
周玉飞,王红军,左云波.基于级联随机共振系统的微弱故障信息特征获取[J].北京信息科技大学学报(自然科学版),2016,31(3):32-36.
[8]LENG Y G,LAI Z H,FAN S B,et al.Study on large parameter stochastic resonance and weak signal detection of two dimensional Duffing oscillator[J].Acta Physica Sinica,2012,61(23):230502.(in Chinese)
冷永刚,赖志慧,范胜波,等.二维Duffing振子的大参数随机共振及微弱信号检测研究[J].物理学报,2012,61(23):230502.
[9]LENG Y G,WANG T Y.Numerical study of two sampling for stochastic resonance extraction of weak signals from strong noise[J].Acta Physica Sinica,2003,52(10):2432-2437.(in Chinese)
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.
[10]ZHANG G,HU T,ZHANG T Q.Detection of weak periodic signals with large parameters based on stochastic resonance[J].Science Technology and Engineering,2015,15(35):189-192.(in Chinese)
张刚,胡韬,张天骐.基于随机共振大参数微弱周期信号检测[J].科学技术与工程,2015,15(35):189-192.
[11]LAI Z H.LENG Y G.Dynamic response and stochastic resonance of three stable system[J].Acta Physica Sinica,2015,64(20):200503.(in Chinese)
赖志慧,冷永刚.三稳系统的动态响应及随机共振[J].物理学报,2015,64(20):200503.
[12]胡岗.随机力与非线性系统[M].上海:上海科技教育出版社,1996.
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