Detection of weak signals in piecewise asymmetric stochastic resonance system

PDF(3984 KB)

Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (5) : 114-122.

HE Lifang, ZHU Wei, ZHANG Tianqi

Author information +

History +

Abstract

Stochastic resonance (SR), as a potential signal processing tool, can enhance weak signal by transforming noise energy and effectively reduce the influence of noise signal on feature extraction. A piecewise nonlinear system model is proposed for the lack of obvious amplitude gain of spectrum on characteristic frequency and low noise utilization in piecewise linear system. The system parameters are independent and easy to adjust, and the optimal stochastic resonance can be induced by adjusting the parameters. Under the bistable model, its Kramers escape rate and output SNR are deduced. Meanwhile, the model formula simulation and numerical simulation are compared with the piecewise symmetric system to demonstrate the effectiveness of the method. The results show that the method can extract the characteristic frequency and has good enhancement performance and anti-noise ability. Finally, the system is applied to different types of the bearing fault detection and the adaptive intelligent algorithm is used to select the optimal system parameters. The results show that the output amplitude of the asymmetric system is 8 times, 3 times and 6 times that of the symmetric system. The data show that the asymmetric system can realize weak feature detection and early fault diagnosis more effectively. This study provides theoretical guidance and basis for the application of the system in practical engineering.

Key words

asymmetric system / Stochastic resonance / Genetic algorithm / Average SNR gain

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

HE Lifang, ZHU Wei, ZHANG Tianqi. Detection of weak signals in piecewise asymmetric stochastic resonance system[J]. Journal of Vibration and Shock, 2022, 41(5): 114-122

References

[1] HE Q, YAN R, KONG F, et al. Machine condition monitoring using principal component representations[J]. Mechanical Systems & Signal Processing, 2009, 23(2):446-466.
[2] N. SAWALHI, RANDALL R B. Vibration response of spalled rolling element bearings: Observations, simulations and signal processing techniques to track the spall size[J]. Mechanical Systems & Signal Processing, 2011, 25(3):846-870.
[3] 王丽华,赵晓平,周子贤，等.基于自适应遗传随机共振的滚动轴承微弱故障诊断[J].现代电子技术,2019,42(20):40-44.
WANG LH, ZHAO X P, ZHOU Z X, et al. Fault diagnosis of rolling bearing based on adaptive genetic stochastic resonance [J]. Modern electronic technology,2019, 42(20):40-44.
[4] BENZI R, SUTERA A, VULPIANI A. The mechanism of stochastic resonance [J]. Journal of physics A：Mathematical and General, 1981, 14: 453-457.
[5] 唐立力,陈国彬.阱宽非对称性诱导随机共振的轴承故障诊断方法[J].科学技术工程，2018，18（14）：197-202.
TANG L L, CHEN G B. Bearing Fault diagnosis Method based on Random resonance induced by Well width Asymmetry [J]. Science and Technology Engineering, 2008, 18 (14) : 197-202.
[6] 胡岗. 随机力与非线性系统[M]. 上海:上海科技教育出版社.1994.
HU G.. Stochastic forces and nonlinear systems [M]. Shanghai: Shanghai Science and Technology Education Press.1994.
[7] QIAO Z J, PAN Z R. SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient [J]. Measurement Science and Technology, 2015.
[8] GUAN Z, LIAO Z , Li K , et al. A Precise Diagnosis Method of Structural Faults of Rotating Machinery based on Combination of Empirical Mode Decomposition, Sample Entropy, and Deep Belief Network[J]. Sensors, 2019, 19(3).
[9] 冷永刚.大信号变尺度随机共振的机理分析及其工程应用研究[D].天津大学,2004.
LENG Y G. Mechanism analysis of large signal variable scale stochastic resonance and its engineering Application [D]. Tianjin University, 2004.
[10] 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(10):2432-2437.
LENG Y G, WANG T Y. Numerical study on secondary sampling for stochastic resonance to extract weak signal from strong noise [J]. Journal of physics. Sin,2003(10):2432-2437.
[11] 范胜波,王太勇,冷永刚,等.基于变尺度随机共振的弱周期性冲击信号的检测[J].中国机械工程,2006(04):387-390.
FAN S B, WANG T Y, LENG Y G et al. Detection of weak periodic impact signal based on variable scale stochastic resonance [J]. China Mechanical Engineering,2006(04):387-390.
[12] 刘利姣,黄光明,杜茜，等.大参数随机共振的两种方法及数值仿真[J].信息与电子工程,2007(03):182-185.
LIU L J, HUANG G M, DU X, et al. Two Methods and Numerical simulation of large parameter stochastic resonance [J]. Information and Electronic Engineering,2007(03):182-185.
[13] 张刚,吴瑕.基于Hilbert的单边带调制随机共振的微弱信号检测[J].电子测量与仪器学报,2019,33(02):10-17.
ZHANG G, WU X. Detection of weak signal based on Hilbert's single sideband modulated stochastic resonance [J]. Journal of electronic measurement and instrumentation,2019,33(02):10-17.
[14] Wang Z , Qiao Z , Zhou L , et al. Array-enhanced logical stochastic resonance subject to colored noise[J]. Chinese Journal of Physics, 2017, 55(2):252-259.
[15] Zhang L , Song A . Realizing reliable logical stochastic resonance under colored noise by adding periodic force[J]. Physica A: Statistical Mechanics and its Applications, 2018, 503.
[16] Zhang L , Zheng W , Xie F , et al. Effect of the correlation between internal noise and external noise on logical stochastic resonance in bistable systems[J]. PHYSICAL REVIEW E, 2017, 96(5):052203.
[17] Zhang L , Zheng W , Song A . Adaptive logical stochastic resonance in time-delayed synthetic genetic networks[J]. Chaos, 2018, 28(4):043117.
[18] LI Z , LIU X , HAN S , et al. Fault diagnosis method and application based on unsaturated piecewise linear stochastic resonance[J]. Review of Scientific Instruments, 2019, 90(6):065112.
[19] QIAO Z J, LEI Y G, LIN, J, et al. An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis[J]. Mechanical Systems & Signal Processing, 2017, 84:731-746.
[20] QIAO Z J , LEI Y G , Lin J , et al. Stochastic resonance subject to multiplicative and additive noise: The influence of potential asymmetries[J]. Physical Review E, 2016, 94(5):052214.
[21] 贺利芳,江川,张刚，等.分段线性非对称系统在故障检测中的研究[J].仪器仪表学报,2020,41(02):226-234.
HE L F, JIANG C, ZHANG G, et al. Study on piecewise linear asymmetric system in fault detection [J]. Chinese journal of instrumentation,2020,41(02):226-234.
[22] 胡茑庆. 随机共振微弱特征信号检测理论与方法[M]. 国防工业出版社, 2012.
HU N Q. Detection theory and Method of stochastic resonance weak characteristic signal [M]. National Defense Industry Press, 2012.
[23] Zhao W L, Wang J, Wang L Z, et al. The unsaturated bistable stochastic resonance system.[J]. Chaos (Woodbury, N.Y.),2013,23(3).
[24] 贺利芳,周熙程,张刚,张天骐.Levy噪声下新型势函数的随机共振特性分析及轴承故障检测[J].振动与冲击,2019,38(12):53-62.
HE L F, ZHOU X C, ZHANG G, et al. Analysis of stochastic resonance characteristics of a new potential function under Levy noise and bearing fault detection [J]. Vibration and impact,2019,38(12):53-62.
[25] 冯建强,孙诗一.四阶龙格—库塔法的原理及其应用[J].数学学习与研究,2017(17):3-5.
FENG J Q, SUN S Y. Principle and Application of fourth-order Runge-Kutta method [J]. Mathematics Learning and Research,2017(17):3-5.
[26] 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）:15 6-163.
ZHANG C, HE Y Y. Adaptive Stochastic resonance and VMD Decomposition based on Genetic Algorithm for Bearing Fault diagnosis [J]. Mechanical Transmission, 2008,42(04):156-163.
[27] 刘进军，冷永刚，张雨阳，等.势函数特征参数调节随机共振及动车轴承故障检测研究[J].振动与冲击,2019,38(13):26-33+41.
LIU J J, LENG Y G, ZHANG Y Y, et al. Research on stochastic resonance and fault detection of bullettrain bearing with potential function characteristic parameters [J]. Vibration and impact,2019,38(13):26-33+41.
[28] B. Wang, Y. Lei, N. Li, et al. A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings[J]. IEEE Transactions on Reliability, 2018:1-12.
[29] CWRU.12k drive end bearing fault data [EB/OL]. [2019-05-15]. http://csegroups. case. du/bearing-datacenter/pages/download-data-file.
[30] 贺利芳,杨玉蕾,张天骐.时延反馈EVG系统随机共振特性研究及轴承故障诊断[J].仪器仪表学报,2019,40(08):47-57.
HE L F, YANG Y L, ZHANG T Q. Research on stochastic resonance characteristics of delay feedback EVG system and bearing fault diagnosis [J]. Journal of Instrumentation,2019,40(08):47-57.
[31] 张刚,宋莹,张天骐.Levy噪声驱动下指数型单稳系统的随机共振特性分析[J].电子与信息学报,2017,39(04):893-900.
Zhang G,Song Y ,Zhang T Q. Analysis of Stochastic Resonance of Exponential Monostable System Driven by Levy Noise [J]. Journal of Electronics & Information Technology,2017,39(04):893-900.