|
|
|
Novel Woods-Saxon stochastic resonance system for weak signal detection |
| Yong-Hui Zhou(周永辉), Xue-Mei Xu(许雪梅), Lin-Zi Yin(尹林子), Yi-Peng Ding(丁一鹏), Jia-Feng Ding(丁家峰), Ke-Hui Sun(孙克辉) |
| School of Physics and Electronics, Central South University, Changsha 410083, China |
|
|
|
|
Abstract We propose a joint exponential function and Woods-Saxon stochastic resonance (EWSSR) model. Because change of a single parameter in the classical stochastic resonance model may cause a great change in the shape of the potential function, it is difficult to obtain the optimal output signal-to-noise ratio by adjusting one parameter. In the novel system, the influence of different parameters on the shape of the potential function has its own emphasis, making it easier for us to adjust the shape of the potential function. The system can obtain different widths of the potential well or barrier height by adjusting one of these parameters, so that the system can match different types of input signals adaptively. By adjusting the system parameters, the potential function model can be transformed between the bistable model and the monostable model. The potential function of EWSSR has richer shapes and geometric characteristics. The effects of parameters, such as the height of the barrier and the width of the potential well, on SNR are studied, and a set of relatively optimal parameters are determined. Moreover, the EWSSR model is compared with other classical stochastic resonance models. Numerical experiments show that the proposed EWSSR model has higher SNR and better noise immunity than other classical stochastic resonance models. Simultaneously, the EWSSR model is applied to the detection of actual bearing fault signals, and the detection effect is also superior to other models.
|
Received: 18 July 2019
Revised: 31 December 2019
Accepted manuscript online:
|
|
PACS:
|
05.40.-a
|
(Fluctuation phenomena, random processes, noise, and Brownian motion)
|
| |
02.50.-r
|
(Probability theory, stochastic processes, and statistics)
|
| |
05.45.-a
|
(Nonlinear dynamics and chaos)
|
| |
43.60.Hj
|
(Time-frequency signal processing, wavelets)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61501525) and the National Natural Science Foundation of Hunan Province of China (Grant No. 2018JJ3680). |
Corresponding Authors:
Xue-Mei Xu
E-mail: xuxuemei999@126.com
|
Cite this article:
Yong-Hui Zhou(周永辉), Xue-Mei Xu(许雪梅), Lin-Zi Yin(尹林子), Yi-Peng Ding(丁一鹏), Jia-Feng Ding(丁家峰), Ke-Hui Sun(孙克辉) Novel Woods-Saxon stochastic resonance system for weak signal detection 2020 Chin. Phys. B 29 040503
|
| [1] |
Peng H H, Xu X MYang B C and Yin L Z 2016 J. Phys. Soc. Jpn. 85 044005
|
| [2] |
Luo J J, Xu X M, Ding Y P, Yang B C, Sun K H and Yin L Z 2018 Eur. Phys. J. Plus 133 239
|
| [3] |
Li C J, Xu X M, Ding Y P and Yin L Z 2018 Int. J. Mod. Phys. B 32 1850103
|
| [4] |
Li M P, Xu X M, Yang B Cand Ding J F 2015 Chin. Phys. B 24 060504
|
| [5] |
Feng J L, Dong J M and Tang X L 2016 Chin. Phys. Lett. 33 108701
|
| [6] |
Kong L W, Zheng W R and Fang H P 2016 Chin. Phys. Lett. 33 020501
|
| [7] |
Zhang J Q, Huang S F, Pang S T, Wang M S and Gao S 2015 Chin. Phys. Lett. 32 120502
|
| [8] |
Liu Y L, Yu X M and Hao Y H 2015 Chin. Phys. Lett. 32 110503
|
| [9] |
Song X T, Li H W, Yin Z Q, Liang W Y and Zhang C M 2015 Chin. Phys. Lett. 32 080302
|
| [10] |
Benzi R, Sutera A and Vulpiani A 1981 J. Phys. A: Math. Gen. 14 L453
|
| [11] |
Chapeau-Blondeau F 1997 Phys. Rev. E 55 2016
|
| [12] |
Mcnamara B, Wiesenfeld K and Roy R 1988 Phys. Rev. Lett. 60 2626
|
| [13] |
Gingl Z, Vajtai R and Kiss L B 2000 Chaos Solitons Fractals. 11 1929
|
| [14] |
Gammaitoni L, Hänggi P and Jung P 1998 Rev. Mod. Phys. 70 223
|
| [15] |
Wiesenfeld K and Moss F 1995 Nature 373 33
|
| [16] |
Wu J J, Leng Y G and Qiao H S 2018 Acta Phys. Sin. 67 210502 (in Chinese)
|
| [17] |
Asdi A S and Tewfik A H 1995 Acoustics, Speech, and Signal Processing, May 9-12, 1995, Detroit, MI, USA, p. 1332
|
| [18] |
Galdi V, Pierro V and Pinto I M 1998 Phys. Rev. E 57 6470
|
| [19] |
Lutz E 2001 Phys. Rev. E 64 051106
|
| [20] |
Leng Y G, Leng Y S, Wang T Y and Guo Y 2006 J. Sound & Vib. 292 788
|
| [21] |
Jin Y F 2018 Chin. Phys. B 27 050501
|
| [22] |
Wang S and Wang F Z 2018 Acta Phys. Sin. 16 160502 (in Chinese)
|
| [23] |
Xu W, Wang L, Feng J Q, Qiao Y and Han P 2018 Chin. Phys. B 27 110503
|
| [24] |
Zhang X H and Liu S Q 2018 Chin. Phys. B 4 040501
|
| [25] |
He Q B and Wang J 2012 Digital Signal Process. 22 614
|
| [26] |
Liu J, Li G, Liu S L and Chen Y Q 2004 Space Med. & Med. Eng. 17 360
|
| [27] |
Li Q Q, Xu X M, Yin Y Z, Ding Y P, Ding J F and Sun K H 2018 Chin. Phys. B 27 034203
|
| [28] |
Wu X J, Guo W M, Cai W S, Shao X G and Pan Z X 2004 Talanta 61 863
|
| [29] |
Shi P M, Ding X J and Han D Y 2014 Measurement 47 540
|
| [30] |
Lu S L, He Q B and Kong F R 2014 Mech. Syst. & Signal Process. 45 488
|
| [31] |
Lu S L, He Q B and Kong F R 2015 Engineering Asset Management-Systems, Professional Practices and Certification (Berlin: Springer) pp. 99-108
|
| [32] |
Zhang G, Shi J B and Zhang T Q 2018 J. Electron. Meas. & Instrum. 32 142
|
| 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
|
|
|
