随机共振是噪声背景下非线性系统对微弱特征信号的最优响应，能够对微弱特征信号进行增强。偏置单稳态系统相较于传统的双稳态系统，对非周期脉冲激励具有良好的共振特性。然而，非线性系统的参数影响系统的最优输出，对不同的非周期脉冲激励，系统难以自适应调节。针对上述问题，本文研究强噪声与非周期脉冲激励下偏置单稳态自适应随机共振. 首先，基于优化算法实现不同非周期脉冲激励下的自适应随机共振。然后，以强噪声背景下钢丝绳漏磁检测信号为应用对象，经偏置单稳态系统自适应随机共振输出后，以峰峰值作为损伤特征演化评价指标，评估不同微弱损伤特征间的差异。同时，应用偏置单稳态自适应随机共振法和自适应移位平均法对强噪声背景下钢丝绳漏磁信号进行对比分析，以峰峰值增益和信噪比增益为指标，结果表明偏置单稳态自适应随机共振法效果优于自适应移位平均法。最后，讨论了噪声强度对单稳态自适应随机共振的影响，发现偏置单稳态自适应随机共振具有较强的抗噪能力。
Abstract
Stochastic resonance is the optimal response of nonlinear system to weak signals in noise background, which can enhance weak signals. Compared with traditional bistable systems, biased monostable systems exhibit good resonance characteristics under non-periodic pulse excitation. However, the parameters of the nonlinear system affect the optimal output of the system. For different aperiodic pulse excitation, the system is difficult to adjust adaptively. To solve these problems, this paper studies the biased monostable adaptive stochastic resonance under strong noise and aperiodic pulse excitation. First, adaptive stochastic resonance under different aperiodic pulse excitation is realized based on optimization algorithm. Then, taking the magnetic flux leakage detection signal of steel wire rope under strong noise background as the application object, after the output of adaptive stochastic resonance of the biased monostable system, the peak-to-peak value is used as the evaluation index of damage feature evolution to evaluate the difference between different weak damage features. At the same time, the bias monostable adaptive stochastic resonance method and the adaptive shift average method are used to compare and analyze the magnetic flux leakage signal of steel wire rope in the strong noise background. With the peak-to-peak gain and signal-to-noise ratio gain as indicators, the results show that the bias monostable adaptive stochastic resonance method is better than the adaptive shift average method. Finally, the influence of noise intensity on monostable adaptive Stochastic resonance is discussed, and it is found that biased monostable adaptive stochastic resonance has strong anti-noise ability.
关键词
非周期脉冲激励 /
强噪声 /
自适应随机共振 /
钢丝绳 /
损伤识别
{{custom_keyword}} /
Key words
aperiodic pulse excitation /
strong noise /
adaptive stochastic resonance /
steel wire rope /
damage identification
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Budrikis Z. Forty years of stochastic resonance [J]. Nature Reviews Physics, 2021, 3(12): 771-771.
[2] 靳艳飞，许鹏飞，李永歌，等. 多稳态动力系统中随机共振的研究进展[J]. 力学进展，2023, 53(2): 1-38.
Jin Yan-fei, Xu Peng-fei, Li Yong-ge, et al. Stochastic resonance of multi-stable dynamical systems: a review [J]. Advances in Mechanics, 2023, 53(2): 1-38.
[3] 宫涛，杨建华，单振，等. 非线性调频信号激励下非线性系统的最优共振响应[J]. 物理学报, 2022, 71(05): 71-78.
Gong Tao, Yang Jian-hua, Shan Zhen, et al. Optimal resonance response of nonlinear system excited by nonlinear frequency modulation signal [J]. Acta Physica Sinica, 2022, 71(05): 71-78.
[4] 贺利芳，朱伟，张天骐. 分段非对称随机共振系统微弱信号检测[J]. 振动与冲击, 2022, 41(05): 114-122.
He Li-fei, Zhu Wei, Zhang Tian-qi, et al. Detection of weak signals in piecewise asymmetric stochastic resonance system [J]. Journal of vibration and shock, 2022, 41(05): 114-122.
[5] 张刚，谢攀，张天骐. α稳定分布噪声下非对称三稳系统的随机共振特性分析[J]. 振动与冲击, 2021, 40(03): 109-116.
Zhang Gang, Xie Pan, Zhang Tian-qi, et al. Stochastic resonance characteristics analysis of an asymmetric tri-stable system under α-stable distributed noise [J]. Journal of vibration and shock, 2021, 40(03): 109-116.
[6] 张广军，徐健学，姚宏. 含噪双稳杜芬振子矩方程的分岔与随机共振 [J]. 力学学报, 2006(02): 283-288.
Zhang Guang-jun, Xu Jian-xue, Yao Hong. The bifurcation of noisy bistable duffing oscillator’smoment equations and stochastic resonance [J]. Chinese Journal of Theoretical and Applied Mechanics, 2006(02): 283-288.
[7] 张莹，王太勇，冷永刚，等. 双稳随机共振的信号恢复研究[J]. 力学学报, 2008(04): 528-534.
Zhang Ying, Wang Tai-yong, Leng Yong-gang, et al. Study on signal recovery in bistable stochastic resonance [J]. Chinese Journal of Theoretical and Applied Mechanics, 2008(04): 528-534.
[8] 谯自健，陈帅，马莉，等. 多稳态匹配随机共振在机械早期故障特征提取中的应用 [J]. 振动与冲击, 2023, 42(11): 87-95.
Qiao Zi-jian, Chen Shuai, Ma Li, et al. Multistable matching stochastic resonance with its application to mechanical incipient fault characteristic extraction [J]. Journal of vibration and shock, 2023, 42(11): 87-95.
[9] López C, Naranjo Á, Lu S, et al. Hidden Markov Model based Stochastic Resonance and its Application to Bearing Fault Diagnosis [J]. Journal of Sound and Vibration, 2022, 528(23): 116890.
[10] Liu J, Hu B, Wang Y. Optimum adaptive array stochastic resonance in noisy grayscale image restoration [J]. Physics Letters A, 2019, 383(13): 1457-1465.
[11] Zhang H, Yu J, Ma Y, et al. Image restoration based on stochastic resonance in a parallel array of Fitzhugh–Nagumo Neuron [J]. Complexity, 2020, 2020: 1-9.
[12] Li J, Wang X, Li Z, et al. Stochastic resonance in cascaded monostable systems with double feedback and its application in rolling bearing fault feature extraction [J]. Nonlinear Dynamics, 2021, 104: 971-988.
[13] Liu W, Liu Z, Zhang Q, et al. Magnetic anomaly signal detection using parallel monostable stochastic resonance system [J]. IEEE Access, 2020, 8: 162230-162237.
[14] Zhang G, Zhou L, Zhang T. Stochastic resonance in a monostable system driven by time-delayed feedback [J]. Indian Journal of Physics, 2021, 95: 99-108.
[15] Liu L, Wang F, Liu Y. Levy noise-driven stochastic resonance in a coupled monostable system [J]. The European Physical Journal B, 2019, 92: 1-9.
[16] Shen M, Yang J, Sanjuán M. A. F, et al. Adaptive denoising for strong noisy images by using positive effects of noise [J]. The European Physical Journal Plus, 2021, 136(6): 698.
[17] Bian H, Guo Z, Zhou C, et al. Research on orderly charge and discharge strategy of EV based on QPSO algorithm [J]. IEEE Access, 2022, 10: 66430-66448.
[18] Shan Z, Yang J, Sanjuán M. A. F, et al. A novel adaptive moving average method for signal denoising in strong noise background [J]. The European Physical Journal Plus, 2022, 137(1): 50.
[19] 黄姗姗，李志农，毛清华，等. 基于经验模态分解的钢丝绳缺陷漏磁检测 [J]. 无损检测, 2023, 45(02): 23-27+59.
Huang Shan-shan, Li Zhi-nong, Mao Qing-hua, et al. The magnetic flux leakage detection of wire ropes based on empirical mode decomposition [J]. Nondestructive Testing, 2023, 45(02): 23-27+59.
[20] 刘庆云，李志舜. 高斯白噪声序列谱的统计特性及应用研究 [J]. 声学与电子工程, 2003, 2003(01): 9-11.
Liu Qing-yun, Li Zhi-shun. Statistical characteristics and application of Gaussian white noise sequence spectrum [J]. Acoustics and Eleclrical Engineering, 2023, 45(02): 23-27+59.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(1650 KB)
