IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v175y2023ip1s0960077923008615.html
   My bibliography  Save this article

Noise-boosted weak signal detection in fractional nonlinear systems enhanced by increasing potential-well width and its application to mechanical fault diagnosis

Author

Listed:
  • Qiao, Zijian
  • He, Yuanbiao
  • Liao, Changrong
  • Zhu, Ronghua

Abstract

Noise is seen as the annoying something for weak signal detection, but noise is beneficial to enhance weak signals of interest embedded in noise in nonlinear systems. Moreover, the fractional-order derivative can reinforce noise-boosted weak signal detection. However, the role of varying potential-well depth and width individually on noise-boosted weak signal detection in overdamped and underdamped fractional-order nonlinear systems has not been investigated yet. For this purpose, this paper designs a special bistable potential with varying potential-well depth and width individually to study their influences on weak signal detection numerically. Then, based on simulated conclusions a noise-boosted weak fault diagnosis method enhanced by increasing potential-well width is proposed to enhance weak fault characteristics of machinery, where the amplitude amplification factor is seen as an indicator to quantify the performance of weak signal detection. Finally, some simulations and a bearing fault experiment with an outer race defect were performed to demonstrate the proposed method. Simulated conclusions are identical to experimental ones and indicate that increasing potential-well width would improve noise-boosted weak signal detection but increasing potential-well depth would weaken it regardless of underdamped or overdamped fractional-order nonlinear systems. Nonetheless, increasing the potential-well width endlessly would make the amplitude at the outer race fault characteristic frequency unchanged. That is because the signal with the constant energy would not accelerate the particles to jump across the wide potential wells adequately, where it is not an optimal SR. The compared results with a classical denoising method demonstrate the superiority of the proposed method further.

Suggested Citation

  • Qiao, Zijian & He, Yuanbiao & Liao, Changrong & Zhu, Ronghua, 2023. "Noise-boosted weak signal detection in fractional nonlinear systems enhanced by increasing potential-well width and its application to mechanical fault diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
  • Handle: RePEc:eee:chsofr:v:175:y:2023:i:p1:s0960077923008615
    DOI: 10.1016/j.chaos.2023.113960
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923008615
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2023.113960?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vishwamittar, & Batra, Priyanka & Chopra, Ribhu, 2021. "Stochastic resonance in two coupled fractional oscillators with potential and coupling parameters subjected to quadratic asymmetric dichotomous noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    2. Akhil Dodda & Aaryan Oberoi & Amritanand Sebastian & Tanushree H. Choudhury & Joan M. Redwing & Saptarshi Das, 2020. "Stochastic resonance in MoS2 photodetector," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. He, Yuzhu & Fu, Yuxuan & Qiao, Zijian & Kang, Yanmei, 2021. "Chaotic resonance in a fractional-order oscillator system with application to mechanical fault diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Mbakob Yonkeu, R. & David, Afungchui, 2022. "Coherence and stochastic resonance in the fractional-birhythmic self-sustained system subjected to fractional time-delay feedback and Lévy noise," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    5. Fu, Peng & Wang, Can-Jun & Yang, Ke-Li & Li, Xu-Bo & Yu, Biao, 2022. "Reentrance-like vibrational resonance in a fractional-order birhythmic biological system," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    6. Qiao, Zijian & Shu, Xuedao, 2021. "Coupled neurons with multi-objective optimization benefit incipient fault identification of machinery," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    7. N. Murray & M. Holman, 2001. "The role of chaotic resonances in the Solar System," Nature, Nature, vol. 410(6830), pages 773-779, April.
    8. Zhang, Wenyue & Shi, Peiming & Li, Mengdi & Han, Dongying, 2021. "A novel stochastic resonance model based on bistable stochastic pooling network and its application," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahmad Taher Azar & Farah Ayad Abdul-Majeed & Hasan Sh. Majdi & Ibrahim A. Hameed & Nashwa Ahmad Kamal & Anwar Jaafar Mohamad Jawad & Ali Hashim Abbas & Wameedh Riyadh Abdul-Adheem & Ibraheem Kasim Ibr, 2022. "Parameterization of a Novel Nonlinear Estimator for Uncertain SISO Systems with Noise Scenario," Mathematics, MDPI, vol. 10(13), pages 1-17, June.
    2. Zhang, Gang & Liu, Xiaoman & Zhang, Tianqi, 2022. "Two-Dimensional Tri-stable Stochastic Resonance system and its application in bearing fault detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    3. Shi, Zhuozheng & Liao, Zhiqiang & Tabata, Hitoshi, 2022. "Boosting learning ability of overdamped bistable stochastic resonance system based physical reservoir computing model by time-delayed feedback," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    4. Subir Ghosh & Andrew Pannone & Dipanjan Sen & Akshay Wali & Harikrishnan Ravichandran & Saptarshi Das, 2023. "An all 2D bio-inspired gustatory circuit for mimicking physiology and psychology of feeding behavior," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. 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).
    6. Muhtasim Ul Karim Sadaf & Najam U Sakib & Andrew Pannone & Harikrishnan Ravichandran & Saptarshi Das, 2023. "A bio-inspired visuotactile neuron for multisensory integration," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    7. Lini Qiu & Guitian He & Yun Peng & Huijun Lv & Yujie Tang, 2023. "Average amplitudes analysis for a phenomenological model under hydrodynamic interactions with periodic perturbation and multiplicative trichotomous noise," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(4), pages 1-20, April.
    8. Livija Cveticanin & Nicolae Herisanu & Ivona Ninkov & Mladen Jovanovic, 2022. "New Closed-Form Solution for Quadratic Damped and Forced Nonlinear Oscillator with Position-Dependent Mass: Application in Grafted Skin Modeling," Mathematics, MDPI, vol. 10(15), pages 1-15, July.
    9. Taiwo O. Roy-Layinde & Kehinde A. Omoteso & Babatunde A. Oyero & John A. Laoye & Uchechukwu E. Vincent, 2022. "Vibrational resonance of ammonia molecule with doubly singular position-dependent mass," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(5), pages 1-11, May.
    10. Li, Jimeng & Cheng, Xing & Peng, Junling & Meng, Zong, 2022. "A new adaptive parallel resonance system based on cascaded feedback model of vibrational resonance and stochastic resonance and its application in fault detection of rolling bearings," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    11. Baysal, Veli & Solmaz, Ramazan & Ma, Jun, 2023. "Investigation of chaotic resonance in Type-I and Type-II Morris-Lecar neurons," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    12. Suo, Jian & Wang, Haiyan & Lian, Wei & Dong, Haitao & Shen, Xiaohong & Yan, Yongsheng, 2023. "Feed-forward cascaded stochastic resonance and its application in ship radiated line signature extraction," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    13. Julyan H. E. Cartwright & Diego L. González & Oreste Piro, 2021. "Dynamical Systems, Celestial Mechanics, and Music: Pythagoras Revisited," The Mathematical Intelligencer, Springer, vol. 43(1), pages 25-39, March.
    14. Claudia A. Pérez-Pinacho & Cristina Verde, 2022. "A Note on an Integral Transformation for the Equivalence between a Fractional and Integer Order Diffusion Model," Mathematics, MDPI, vol. 10(5), pages 1-13, February.
    15. Huang, Pengfei & Chai, Yi & Chen, Xiaolong, 2022. "Multiple dynamics analysis of Lorenz-family systems and the application in signal detection," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    16. 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).
    17. Muhammad Zuhaib & Faraz Ahmed Shaikh & Wajiha Tanweer & Abdullah M. Alnajim & Saleh Alyahya & Sheroz Khan & Muhammad Usman & Muhammad Islam & Mohammad Kamrul Hasan, 2022. "Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment," Energies, MDPI, vol. 15(21), pages 1-32, October.
    18. Zhou, Zhao-Xiang & Yang, Ke-Li & Wang, Can-Jun & Yu, Biao & Li, Xu-Bo & Su, Yue-Wen, 2023. "Theory and numerics of vibrational resonance in a three-level atomic optical bistable system," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    19. Xie, Tianting & Ji, Yuandong & Yang, Zhongshan & Duan, Fabing & Abbott, Derek, 2023. "Optimal added noise for minimizing distortion in quantizer-array linear estimation," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    20. Zhang, Dongjian & Ma, Qihua & Dong, Hailiang & Liao, He & Liu, Xiangyu & Zha, Yibin & Zhang, Xiaoxiao & Qian, Xiaomin & Liu, Jin & Gan, Xuehui, 2023. "Time-delayed feedback bistable stochastic resonance system and its application in the estimation of the Polyester Filament Yarn tension in the spinning process," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:175:y:2023:i:p1:s0960077923008615. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.