吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (3): 1091-1096.doi: 10.13229/j.cnki.jdxbgxb20200260

• 通信与控制工程 • 上一篇    

自适应耦合周期势系统随机共振信号增强方法

李伟1,2(),陈剑1,2(),陶善勇1,2   

  1. 1.合肥工业大学 噪声振动工程研究所，合肥 230009
    2.安徽省汽车NVH工程技术研究中心，合肥 230009
  • 收稿日期:2020-04-19 出版日期:2021-05-01 发布日期:2021-05-07
  • 通讯作者: 陈剑 E-mail:jazzlee@mail.hfut.edu.cn;Chenjian@hfut.edu.cn
  • 作者简介:李伟（1993-），男，博士研究生. 研究方向：信号处理与汽车故障诊断. E-mail:jazzlee@mail.hfut.edu.cn
  • 基金资助:
    安徽省科技重大专项项目(17030901049)

Method of enhancing stochastic resonance signal of self⁃adaptive coupled periodic potential system

Wei LI1,2(),Jian CHEN1,2(),Shan-yong TAO1,2   

  1. 1.Institute of Noise and Vibration Engineering，Hefei University of Technology，Hefei 230009，China
    2.Automotive NVH Engineering & Technology Research Center of Anhui Province，Hefei 230009，China
  • Received:2020-04-19 Online:2021-05-01 Published:2021-05-07
  • Contact: Jian CHEN E-mail:jazzlee@mail.hfut.edu.cn;Chenjian@hfut.edu.cn

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摘要：

在单一周期势系统的基础上，提出了一种随机共振粒子群优化算法，用于增强和提取滚动轴承微弱故障特征。该方法采用粒子群优化算法实现自适应耦合周期势系统的系统参数、耦合系数和步长的自适应匹配，以达到增强目标信号、提高其信噪比的目的。通过工程试验验证结果表明：①由于耦合系统的作用，控制系统通过调节参数来影响被控系统的随机共振，从而使得自适应耦合周期势系统随机共振方法在微弱故障特征信号增强上更优于单一周期势系统随机共振方法；②由于控制系统随机共振与被控系统随机共振的相互协同作用，大大增强了被控系统的随机共振效应，从而使得自适应双输入耦合周期势系统随机共振方法比单输入耦合周期势系统随机共振方法更适用于工程实际中噪声环境下微弱故障特征信号的提取。

关键词: 信号处理, 周期势函数, 耦合系统, 故障诊断, 信噪比, 自适应

Abstract:

Based on the single periodic potential system， a new method of stochastic resonance signal enhancement for coupled periodic potential systems was proposed. The method uses particle swarm optimization to achieve adaptive matching of system parameters， coupling coefficients and step sizes. Using this method the targeting signal and the signal-noice-ratio can be enhanced. Experiments are carried out to verify the ne method. The results show that due to the function of the coupling system， the control system affects the stochastic resonance of the controlled system by adjusting the parameters， so that the adaptive coupling periodic potential system stochastic resonance method performs better than the adaptive first-order periodic potential system stochastic resonance method in the enhancement of weak fault characteristic signal. Due to the synergy between the stochastic resonance of the control system and the stochastic resonance of the controlled system， the stochastic resonance effect of the controlled system is greatly enhanced. Therefore， the adaptive double-input coupling periodic potential system stochastic resonance method is more suitable for the extraction of weak fault characteristic signals in the noise environment than the adaptive single-input coupled periodic potential system stochastic resonance method.

Key words: signal processing, periodic potential function, coupling system, fault diagnosis, signal noise ratio（SNR）, self-adaptation

中图分类号: 

  • TN911.7

图1

输入、输出流程图"

图2

势函数三维图a=1, b=1, r=0.02"

图3

航空发动机轴承试验机"

图4

内圈故障状态"

图5

内圈外滚道故障振动信号时域波形及频谱"

图6

内圈外滚道故障振动信号输出信噪比随迭代次数的变化曲线"

图7

内圈外滚道故障输出频谱"

1 林敏,黄咏梅,方利民. 耦合双稳系统的随机共振控制[J]. 物理学报,2008,57(4):2048-2052.
Lin Min, Huang Yong-mei, Fang Li-min. The stochastic resonance control of coupled bistable systems [J]. Acta Physica Sinica, 2008, 57(4):2048-2052.
2 郑俊,林敏. 耦合双稳系统随机共振的轴承故障检测方法[J]. 中国计量学院学报,2014,25(1):51-56.
Zheng Jun, Lin Min. Detection of fault signals based on coupled bistable systems stochastic resonance[J]. Journal of China University of Metrology, 2014,25(1):51-56.
3 林敏,方利民,朱若谷. 双频信号作用下耦合双稳系统的双共振特性[J]. 物理学报,2008,57(5):2638-2642.
Lin Min, Fang Li-min, Zhu Ruo-gu. The dual-resonance characteristic of coupled bistable system affected by two-frequency signal[J]. Acta Physica Sinica, 2008, 57(5):2638-2642.
4 Li Q Q, Xu X M, Yin L Z, et al. Implication of two-coupled tri-stable stochastic resonance in weak signal detection[J]. Chinese Physics B, 2018, 27(3):034203.
5 杨亭亭,张慧清. 二值噪声激励下耦合欠阻尼双稳系统的随机共振[J]. 动力学与控制学报,2016,14(1):70-74.
Yang Ting-ting, Zhang Hui-qing. Stochastic resonance in coupled underdamped bistable systems driven by symmetric dichotomous noises[J]. Journal of Dynamics and Control, 2016, 14(1):70-74.
6 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.
7 罗毅. 自适应二阶双稳态随机共振的微弱特征增强检测方法研究[J]. 中国测试,2017,43(6):31-36.
Luo Yi. Adaptive second-order bistable stochastic resonance method and its application in weak characteristic enhance detection[J]. China Measurement & Test, 2017,43(6):31-36.
8 时培明,孙鹏,袁丹真. 基于非线性耦合双稳态随机共振的轴承微弱故障信号增强检测方法研究[J]. 计量学报,2018,39(3):373-376.
Shi Pei-ming, Sun Peng, Yuan Dan-zhen. Research on the enhanced detection method of bearing fault weak fault signal based on nonlinear coupled bistable stochastic resonance[J]. Acta Metrologica Sinica, 2018, 39(3):373-376.
9 Li J M, Zhang J F, Li M, et al. A novel adaptive stochastic resonance method based on coupled bistable systems and its application in rolling bearing fault diagnosis[J]. Mechanical Systems and Signal Processing,2019,114:128-145.
10 孔德阳,彭华,马金金. 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):1864-1871.
Kong De-yang, Peng Hua, Ma Jin-jin. Adaptive stochastic resonance method on artificial -fish swarm optimization[J]. Acta Electronica Sinica, 2017, 45(8):1864-1871.
11 于繁华,刘仁云,张义民,等. 机械零部件动态可靠性稳健优化设计的群智能算法[J]. 吉林大学学报:工学版,2017,47(6):1903-1908.
Yu Fan-hua, Liu Ren-yun, Zhang Yi-min, et al. Swarm intelligence algorithm for robust optimization design of dynamic reliability of mechanical parts[J]. Journal of Jilin University (Engineering and Technology Edition), 2017,47(6): 1903-1908.
12 李一兵,杨鹏,叶方,等. 基于交互势函数和均场参数估计的分层MRF模型的纹理图像分割[J]. 吉林大学学报:工学版,2015,45(6):2075-2079.
Li Yi-bing, Yang Peng, Ye Fang, et al. Texture image segmentation based on hierarchical MRF model based on interactive potential function and mean field parameter estimation[J]. Journal of Jilin University(Engineering and Technology Edition), 2015,45(6):2075-2079.
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