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Weak signal detection method based on novel composite multistable stochastic resonance |
| Shangbin Jiao(焦尚彬)1, Rui Gao(高蕊)1,2,†, Qiongjie Xue(薛琼婕)1, and Jiaqiang Shi(史佳强)1 |
1 Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China;
2 School of Electronic and Electrical Engineering, Baoji University of Arts and Sciences, Baoji 721016, China |
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Abstract The weak signal detection method based on stochastic resonance is usually used to extract and identify the weak characteristic signal submerged in strong noise by using the noise energy transfer mechanism. We propose a novel composite multistable stochastic-resonance (NCMSR) model combining the Gaussian potential model and an improved bistable model. Compared with the traditional multistable stochastic resonance method, all the parameters in the novel model have no symmetry, the output signal-to-noise ratio can be optimized and the output amplitude can be improved by adjusting the system parameters. The model retains the advantages of continuity and constraint of the Gaussian potential model and the advantages of the improved bistable model without output saturation, the NCMSR model has a higher utilization of noise. Taking the output signal-to-noise ratio as the index, weak periodic signal is detected based on the NCMSR model in Gaussian noise and α noise environment respectively, and the detection effect is good. The application of NCMSR to the actual detection of bearing fault signals can realize the fault detection of bearing inner race and outer race. The outstanding advantages of this method in weak signal detection are verified, which provides a theoretical basis for industrial practical applications.
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Received: 27 October 2022
Revised: 14 November 2022
Accepted manuscript online: 22 November 2022
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PACS:
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02.60.Cb
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(Numerical simulation; solution of equations)
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05.40.-a
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(Fluctuation phenomena, random processes, noise, and Brownian motion)
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05.40.Fb
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(Random walks and Levy flights)
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05.45.-a
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(Nonlinear dynamics and chaos)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61871318), the Key Research and Development Projects in Shaanxi Province (Grant No. 2023-YBGY-044), and the Key Laboratory System Control and Intelligent Information Processing (Grant No. 2020CP10). |
Corresponding Authors:
Rui Gao
E-mail: gaorui@bjwlxy.edu.cn
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Cite this article:
Shangbin Jiao(焦尚彬), Rui Gao(高蕊), Qiongjie Xue(薛琼婕), and Jiaqiang Shi(史佳强) Weak signal detection method based on novel composite multistable stochastic resonance 2023 Chin. Phys. B 32 050202
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[1] Guo Y T, Zhou P, Yao Z and Ma J 2021 Nonlinear Dyn. 105 3603
[2] Zhou Z B, Yu W X, Wang J N and Liu M T 2022 Chaos, Solitons & Fractals 154 111642
[3] Mba C U, Makis V, Marchesiello S, Fasana A and Garibaldi L 2018 Measurement 126 76
[4] Li J M, Wang X D and Zhang X D 2021 Nonlinear Dyn. 104 971
[5] Qiao Z J, Elhattab A, Shu X D and He C B 2021 Nonlinear Dyn. 106 707
[6] Yang Y B, Tao W M, Huang J M and Xu B H 2012 Chin. Phys. B 21 060503
[7] Qiao Z J and Shu X D 2021 Chaos, Solitons and Fractals 145 110813
[8] Mcnamara B, Wiesenfeld K and Roy R 1988 Phys. Rev. Lett. 60 2626
[9] He M, Xu W and Sun Z 2015 Nonlinear Dyn. 79 1787
[10] Zhong S, Zhang L, Wang H, Ma H and Luo M 2017 Nonlinear Dyn. 89 1327
[11] Kim H, Tai W C, Zhou S X and Zuo L 2017 Smart Mater. Struct. 26 115011
[12] Lu S, He Q and Wang J 2019 Mech. Syst. Signal Process. 116 230
[13] Zhang G, Song Y and Zhang T Q 2017 Chin. J. Phys. 55 85
[14] Jin Y F 2018 Chin. Phys. B 27 050501
[15] Liu H G, Liu X L, Yang J H, Sanjuan M A F and Cheng G 2017 Nonlinear Dyn. 89 2621
[16] Yao Y G 2021 Chin. Phys. B 30 060503
[17] Shi T T, XU X M, Sun K H, Ding Y P and Huang W G 2020 Chin. Phys. B 29 050501
[18] Qiao Z J, Lei Y G, Lin J and Jia F 2017 Mech. Syst. Signal Process. 84 731
[19] Wang Z J and Wang L 2013 Chaos 23 33117
[20] Goask M, Perc M and Kralj S 2011 Eur. Phys. J. B 80 519
[21] Jiao S B, Lei S, Jiang W, Zhang Q and Huang W C 2019 IEEE Access 7 160191
[22] Du T H, Cheng X, Sun S G, Hao J, Wang R X and Liang J 2020 Instrument Technique and Sensor 450(07) 100 (in Chinese)
[23] Zhang G and Gao J P 2018 Comput. Appl. 38 2747 (in Chinese)
[24] Jiao S B, Qiao X X, Lei S and Jiang W 2019 Chin. J. Phys. 59 138
[25] He L F, Cui Y Y, Zhang T Q, Zhang G and Song Y 2016 Chin. Phys. B 25 060501
[26] He Q B, Wu E H and Pan Y Y 2018 J. Sound Vibr. 420 174
[27] Li J M, Chen X F, Du Z H, Fang Z W and He Z J 2013 Renew. Energy 60 7
[28] Zeng L Z and Xu B H 2010 J. Phys. A: Statist. Mech. Appl. 389 5128
[29] Han D Y, Su X and Shi P M 2018 Chin. J. Phys. 56 1559
[30] Zhang H B, He Q, Lu S and Kong F 2014 Math. Problems Eng. 2014 315901
[31] Ma T C, Xu F Y, Hu J Z, Song D and Cao S S 2021 Chin. J. Phys. 74 279
[32] Wang Y, Jiao S B, Zhang Q, Lei S and Qiao X X 2018 Chin. J. Phys. 56 1187
[33] Wang B, Lei Y G, Li N P and Li N B 2020 IEEE Trans. Reliab. 69 401
[34] Lei Y G, Han T Y and Wang B 2019 J. Mech. Eng. 55 1 (in Chinese)
[35] Zhang G, Hu D and Zhang T 2019 IEEE Access 7 58435 |
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