|
|
Characteristics of piecewise linear symmetric tri-stable stochastic resonance system and its application under different noises |
Gang Zhang(张刚)1, Yu-Jie Zeng(曾玉洁)1,†, and Zhong-Jun Jiang(蒋忠均)2 |
1 School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications(CQUPT), Chongqing 400065, China; 2 Cyberspace Administration of Guizhou Province, Guiyang 550000, China |
|
|
Abstract Weak signal detection has become an important means of mechanical fault detections. In order to solve the problem of poor signal detection performance in classical tristable stochastic resonance system (CTSR), a novel unsaturated piecewise linear symmetric tristable stochastic resonance system (PLSTSR) is proposed. Firstly, by making the analysis and comparison of the output and input relationship between CTSR and PLSTSR, it is verified that the PLSTSR has good unsaturation characteristics. Then, on the basis of adiabatic approximation theory, the Kramers escape rate, the mean first-passage time (MFPT), and output signal-to-noise ratio (SNR) of PLSTSR are deduced, and the influences of different system parameters on them are studied. Combined with the adaptive genetic algorithm to synergistically optimize the system parameters, the PLSTSR and CTSR are used for numerically simulating the verification and detection of low-frequency, high-frequency, and multi-frequency signals. And the results show that the SNR and output amplitude of the PLSTSR are greatly improved compared with those of the CTSR, and the detection effect is better. Finally, the PLSTSR and CTSR are applied to the bearing fault detection under Gaussian white noise and Levy noise. The experimental results also show that the PLSTSR can obtain larger output amplitude and SNR, and can detect fault signals more easily, which proves that the system has better performance than other systems in bearing fault detection, and has good theoretical significance and practical value.
|
Received: 25 January 2022
Revised: 20 February 2022
Accepted manuscript online: 25 February 2022
|
PACS:
|
05.40.-a
|
(Fluctuation phenomena, random processes, noise, and Brownian motion)
|
|
05.45.-a
|
(Nonlinear dynamics and chaos)
|
|
05.40.Fb
|
(Random walks and Levy flights)
|
|
02.60.Cb
|
(Numerical simulation; solution of equations)
|
|
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61771085), the Research Project of Chongqing Educational Commission, China (Grant Nos. KJ1600407 and KJQN201900601), and the Natural Science Foundation of Chongqing, China (Grant No. cstc2021jcyj-msxmX0836). |
Corresponding Authors:
Yu-Jie Zeng
E-mail: 1156885177@qq.com
|
Cite this article:
Gang Zhang(张刚), Yu-Jie Zeng(曾玉洁), and Zhong-Jun Jiang(蒋忠均) Characteristics of piecewise linear symmetric tri-stable stochastic resonance system and its application under different noises 2022 Chin. Phys. B 31 080502
|
[1] Randall R B and Jérme Antoni 2011 Mechanical Systems and Signal Processing 25 485 [2] Li J, Zhang J, Li M and Zhang Y 2019 Mechanical Systems & Signal Processing 114 128 [3] Raad A, Antoni J and Sidahmed M 2008 Mechanical Systems & Signal Processing 22 574 [4] Gryllias K C and Antoniadis I A 2012 Engineering Applications of Artificial Intelligence 25 326 [5] Gan C, Wu J, Yang S, Hu Y and Cao W 2016 IEEE Transactions on Energy Conversion 31 303 [6] Li Z and Shi B 2016 Shock and Vibration 2016 2841249 [7] Jiang H, Chen J, Dong G, Liu T and Chen G 2015 Mechanical Systems & Signal Processing 52-53 338 [8] Benzi R, Sutera A and Vulpiani A 1999 J. Phys. Chem. 14 L453 [9] Zhang G, Jiang C and Zhang T Q 2020 IEEE Access 8 173710 [10] Yong G L, Yong S L, Tai Y W and Yan G 2006 Journal of Sound & Vibration 292 788 [11] Leng Y G and Wang T Y 2003 Acta Phys. Sin. 52 2432 (in Chinese) [12] Wang L Z, Zhao W L and Chen X 2012 Acta Phys. Sin. 61 160501 (in Chinese) [13] Zhang G, Gao J P and Li H W 2018 Computer Science 45 146 (in Chinese) [14] Wang S, Niu P, Guo Y, Wang F and Han S 2020 IEEE Access 8 73320 [15] Qiao Z J, Ahmed Elhattab, Shu X D and He C B 2021 Nonlinear Dynamics 106 707 [16] Li Z X, Liu X D, Han S J, Wang J G and Ren X P 2019 Rev. Sci. Instrum. 90 065112 [17] Zhao S, Shi P and Han D 2020 Measurement 168 108374 [18] Jiao S B, Lei S, Jiang W, Zhang Q and Huang W C 2019 IEEE Access 7 7 [19] He L F, Zhou X C, Zhang G and Zhang T Q 2019 Journal of Vibration and Shock 38 53 (in Chinese) [20] Zhang G, Song Y and Zhang T Q 2017 Journal of Electronics & Information Technology 39 893 (in Chinese) [21] Zhang G, Zhou L and Zhang T Q 2018 Journal of Electronic Measurement and Instrumentation 32 134 (in Chinese) [22] Zhang G, Tan C L and He L F 2021 Chin. J. Sci. Instrum. 42 228 (in Chinese) [23] Han D Y and Shi P M 2021 Chin. J. Phys. 69 98 [24] Zhang C and He Y Y 2018 J. Mech. Trans. 42 156 (in Chinese) [25] Lai Z H, Wang SB, Zhang G Q, Zhang C L and Zhang J W 2020 Shock and Vibration 2020 6096024 [26] Zheng Y, Wang K, Fu X E, Li Y N and Xue P 2020 Machinery Building & Automation 49 192 (in Chinese) [27] Jiao S B, Li J, Zhang Q and Xie G 2016 Journal of System Simulation 28 139 [28] Gu R C, Xu Y, Zhang H Q and Sun Z K 2011 Acta Phys. Sin. 60 110514 (in Chinese) [29] Nguyen, Quoc, Hung, Sy, Dzung, Nguyen, Choi and Seung-Bok 2015 Mechanical Systems & Signal Processing 56/57 288 [30] Hu G 1994 Stochastic Forces and Nonlinear Systems (Shanghai:Shanghai Science and Technology Education Press) p. 219 [31] Qiao Z J, Liu J, Ma X and Liu J L 2021 Journal of the Franklin Institute 358 2194 [32] Qiao Z J and Shu X D 2021 Chaos, Solitons & Fractals 145 110813 [33] Luo W M and Zhang Y R 2018 Electron. Lett. 54 280 [34] Lu S L, Su Y S, Zhao J W, He Q B, Liu F and Liu Y B 2018 Journal of Vibration and Shock 37 7 (in Chinese) [35] He L F, Hu D Y, Zhang G and Lu S L 2019 Modern Phys. Lett. B 33 19 [36] Zhang G L, Lv X L and Kang Y M 2012 Acta Phys. Sin. 61 040501 (in Chinese) |
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|