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A novel underdamped continuous unsaturation bistable stochastic resonance method and its application

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  • Li, Mengdi
  • Shi, Peiming
  • Zhang, Wenyue
  • Han, Dongying

Abstract

In view of the saturation problem of classical bistable stochastic resonance (CBSR) system, an underdamped continuous unsaturation bistable stochastic resonance (UCUBSR) system is investigated and the feasibility of the system for weak fault feature extraction is discussed. Firstly, based on the reason of saturation phenomenon, a new potential function is constructed, which can independently adjust the steepness of the potential walls. The signal to noise ratio (SNR) of the system is derived. Secondly, a modified unsaturation index, amplitude gain, is proposed, and the influence of the steepness on SR based on SNR and amplitude gain is analyzed. The simulation analysis shows that the amplitude gain can be considered as an appropriate index to quantify SR and has a certain compensation effect on the SNR applied to evaluate unsaturation performance. Finally, from the simulation and experiment, a larger amplitude gain indicates that the proposed system has better unsaturation and weak fault detection ability compared with unsaturation bistable stochastic resonance (UBSR), and a higher amplitude of the output signal at the characteristic frequency proves that the amplitude gain as an indicator can further guide the parameter adjustment of SR and facilitate faint fault signature identification.

Suggested Citation

  • Li, Mengdi & Shi, Peiming & Zhang, Wenyue & Han, Dongying, 2021. "A novel underdamped continuous unsaturation bistable stochastic resonance method and its application," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    • Handle: RePEc:eee:chsofr:v:151:y:2021:i:c:s0960077921005828
    DOI: 10.1016/j.chaos.2021.111228
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    References listed on IDEAS

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    1. Li, Mengdi & Shi, Peiming & Zhang, Wenyue & Han, Dongying, 2020. "Study on the optimal stochastic resonance of different bistable potential models based on output saturation characteristic and application," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    Cited by:

    1. Duan, Lingling & Ren, Yuhao & Duan, Fabing, 2022. "Adaptive stochastic resonance based convolutional neural network for image classification," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
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    3. Ren, Yuhao & Pan, Yan & Duan, Fabing, 2022. "SNR gain enhancement in a generalized matched filter using artificial optimal noise," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    4. Yang, GuiJiang & Ai, Hao & Liu, Wei & Wang, Qiubao, 2023. "Weak signal detection based on variable-situation-potential with time-delay feedback and colored noise," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).

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