IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v164y2022ics0960077922008815.html
   My bibliography  Save this article

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

Author

Listed:
  • Li, Jimeng
  • Cheng, Xing
  • Peng, Junling
  • Meng, Zong

Abstract

Accurate extraction of weak feature information in strong background noise is a key to detect and identify rolling bearing faults. Stochastic resonance (SR) and vibrational resonance (VR) have received extensive attention and research in weak signal detection by reason of their advantages of utilizing additional inputs (i.e. noise or high frequency harmonic signals) to enhance weak signals. Considering the advantages and disadvantages of SR and VR in weak signal detection, this paper combines the two to construct a cascaded feedback model of VR and SR, and utilize it to form a parallel resonance system, which improves the detection performance of weak signals through the ensemble average effect. Furthermore, a multi-parameter optimization strategy based on the improved whale optimization algorithm (WOA) is proposed for the parameter selection of the parallel resonance system. It uses the constructed measurement index independent of the prior knowledge as the fitness function to realize automatic adjustment of multi-parameter, and obtains the final output by weighted summation of the optimal results obtained by multiple iterations. Finally, the suggested method is analyzed by numerical simulation signal and experimental data of rolling bearings, and the effectiveness and superiority of the proposed method in the detection of weak fault features are verified.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:164:y:2022:i:c:s0960077922008815
    DOI: 10.1016/j.chaos.2022.112702
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922008815
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112702?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. He, Deqiang & Liu, Chenyu & Jin, Zhenzhen & Ma, Rui & Chen, Yanjun & Shan, Sheng, 2022. "Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning," Energy, Elsevier, vol. 239(PB).
    2. Usama, B.I. & Morfu, S. & Marquie, P., 2021. "Vibrational resonance and ghost-vibrational resonance occurrence in Chua’s circuit models with specific nonlinearities," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    3. Cong Wang & Meng Gan & Chang’an Zhu, 2019. "A supervised sparsity-based wavelet feature for bearing fault diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 229-239, January.
    4. 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).
    5. 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).
    6. Lu, Shibao & Ye, Weiwei & Xue, Yangang & Tang, Yao & Guo, Min, 2020. "Dynamic feature information extraction using the special empirical mode decomposition entropy value and index energy," Energy, Elsevier, vol. 193(C).
    7. Melani, Arthur Henrique de Andrade & Michalski, Miguel Angelo de Carvalho & da Silva, Renan Favarão & de Souza, Gilberto Francisco Martha, 2021. "A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Nawar, Mohamed A.A. & Hameed, H.S. Abdel & Ramadan, A. & Attai, Youssef A. & Mohamed, M.H., 2021. "Experimental and numerical investigations of the blade design effect on Archimedes Spiral Wind Turbine performance," Energy, Elsevier, vol. 223(C).
    9. Ren, Yuhao & Duan, Fabing, 2016. "Theoretical and experimental implementation of vibrational resonance in an array of hard limiters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 319-326.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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).

    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. 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).
    2. 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).
    3. Refaie, Abdelaziz G. & Abdel Hameed, H.S. & Nawar, Mohamed A.A. & Attai, Youssef A. & Mohamed, Mohamed H., 2021. "Qualitative and quantitative assessments of an Archimedes Spiral Wind Turbine performance augmented by A concentrator," Energy, Elsevier, vol. 231(C).
    4. Kai Xu & Youguang Guo & Gang Lei & Jianguo Zhu, 2023. "A Review of Flywheel Energy Storage System Technologies," Energies, MDPI, vol. 16(18), pages 1-32, September.
    5. Ke Song & Huiting Huan & Yuchi Kang, 2022. "Aerodynamic Performance and Wake Characteristics Analysis of Archimedes Spiral Wind Turbine Rotors with Different Blade Angle," Energies, MDPI, vol. 16(1), pages 1-18, December.
    6. Li, Jiangkuan & Lin, Meng & Li, Yankai & Wang, Xu, 2022. "Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions," Energy, Elsevier, vol. 254(PB).
    7. Fang, Xiaoyu & Qu, Jianfeng & Chai, Yi, 2023. "Self-supervised intermittent fault detection for analog circuits guided by prior knowledge," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    8. Abdel Hameed, Hossam S. & Hashem, Islam & Nawar, Mohamed A.A. & Attai, Youssef A. & Mohamed, Mohamed H., 2023. "Shape optimization of a shrouded Archimedean-spiral type wind turbine for small-scale applications," Energy, Elsevier, vol. 263(PB).
    9. Zhang, Lang & He, Deqiang & He, Yan & Liu, Bin & Chen, Yanjun & Shan, Sheng, 2022. "Real-time energy saving optimization method for urban rail transit train timetable under delay condition," Energy, Elsevier, vol. 258(C).
    10. Liu, Jie & Xu, Yubo & Wang, Lisong, 2022. "Fault information mining with causal network for railway transportation system," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    11. Hu, Wenyu & E, Jiaqiang & Zhang, Feng & Chen, Jingwei & Ma, Yinjie & Leng, Erwei, 2022. "Investigation on cooperative mechanism between convective wind energy harvesting and dust collection during vehicle driving on the highway," Energy, Elsevier, vol. 260(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. Xu, Yadong & Yan, Xiaoan & Feng, Ke & Sheng, Xin & Sun, Beibei & Liu, Zheng, 2022. "Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    14. 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).
    15. Zheng, Niannian & Luan, Xiaoli & Shardt, Yuri A.W. & Liu, Fei, 2024. "Dynamic-controlled principal component analysis for fault detection and automatic recovery," Reliability Engineering and System Safety, Elsevier, vol. 241(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. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Dually attentive multiscale networks for health state recognition of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    18. Kamal, Ahmed M. & Nawar, Mohamed A.A. & Attai, Youssef A. & Mohamed, Mohamed H., 2023. "Archimedes Spiral Wind Turbine performance study using different aerofoiled blade profiles: Experimental and numerical analyses," Energy, Elsevier, vol. 262(PB).
    19. Refaie, Abdelaziz G. & Hameed, H.S. Abdel & Nawar, Mohamed A.A. & Attai, Youssef A. & Mohamed, Mohamed H., 2022. "Comparative investigation of the aerodynamic performance for several Shrouded Archimedes Spiral Wind Turbines," Energy, Elsevier, vol. 239(PC).
    20. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process," Reliability Engineering and System Safety, Elsevier, vol. 231(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:164:y:2022:i:c:s0960077922008815. 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.