IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v250y2024ics0951832024003144.html
   My bibliography  Save this article

The amplitude modulation bispectrum: A weak modulation features extracting method for bearing fault diagnosis

Author

Listed:
  • Yang, Miaorui
  • Zhang, Kun
  • Sheng, Zhipeng
  • Zhang, Xiangfeng
  • Xu, Yonggang

Abstract

As a key component of the industrial equipment, the failure of rolling bearings could cause equipment to malfunction, resulting in serious losses. However, the bearing fault information is often disturbed by the operation of other parts in the industrial equipment. A key issue is the recognition of weak information in the fault diagnosis of rolling bearings. The amplitude modulation bispectrum proposed in this paper is a rolling bearing fault diagnosis method for weak modulation feature extraction. Aiming at the problem that the bearing fault information is weak and difficult to extract, the method reconfigures the amplitude of the signal in the frequency domain to adjust the proportions of different components in the signal effectively and highlight the fault characteristic information. Based on the use of advanced demodulation tool to deal with the complex modulation components in the signal, an index, named bispectrum signal-to-noise ratio, to evaluate the fault information quantitatively for two-dimensional data is also proposed. The index helps to optimize the bispectrum demodulation results and make the valid information clearer. The effectiveness of this method in rolling bearing fault diagnosis is confirmed using simulation and experimental signals, and comparison with other methods has demonstrated the superiority of this method.

Suggested Citation

  • Yang, Miaorui & Zhang, Kun & Sheng, Zhipeng & Zhang, Xiangfeng & Xu, Yonggang, 2024. "The amplitude modulation bispectrum: A weak modulation features extracting method for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:reensy:v:250:y:2024:i:c:s0951832024003144
    DOI: 10.1016/j.ress.2024.110241
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024003144
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110241?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. Dong, Yutong & Jiang, Hongkai & Yao, Renhe & Mu, Mingzhe & Yang, Qiao, 2024. "Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Guo, Jianchun & Si, Zetian & Liu, Yi & Li, Jiahao & Li, Yanting & Xiang, Jiawei, 2022. "Dynamic time warping using graph similarity guided symplectic geometry mode decomposition to detect bearing faults," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Yan, Shen & Shao, Haidong & Min, Zhishan & Peng, Jiangji & Cai, Baoping & Liu, Bin, 2023. "FGDAE: A new machinery anomaly detection method towards complex operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    4. Xia, Jingyan & Huang, Ruyi & Chen, Zhuyun & He, Guolin & Li, Weihua, 2023. "A novel digital twin-driven approach based on physical-virtual data fusion for gearbox fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    5. Len Gelman & Krzysztof Soliński & Andrew Ball, 2021. "Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems," Energies, MDPI, vol. 14(20), pages 1-18, October.
    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. Ma, Chenyang & Wang, Xianzhi & Li, Yongbo & Cai, Zhiqiang, 2024. "Broad zero-shot diagnosis for rotating machinery with untrained compound faults," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    2. Yu, Tian & Li, Chaoshun & Huang, Jie & Xiao, Xiangqu & Zhang, Xiaoyuan & Li, Yuhong & Fu, Bitao, 2024. "ReF-DDPM: A novel DDPM-based data augmentation method for imbalanced rolling bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    3. Li, Yan-Fu & Zhao, Wei & Zhang, Chen & Ye, Jiantao & He, Huiru, 2024. "A study on the prediction of service reliability of wireless telecommunication system via distribution regression," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    4. Chen, Edward & Bao, Han & Dinh, Nam, 2024. "Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    5. Dai, Menghang & Liu, Zhiliang & Wang, Jinrui & Zuo, Mingjian, 2024. "Physics-driven feature alignment combined with dynamic distribution adaptation for three-cylinder drilling pump cross-speed fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    6. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. Xiang, Jiawei & Guo, Jianchun & Li, Xiaoqi, 2024. "A two-stage Duffing equation-based oscillator and stochastic resonance for mechanical fault diagnosis," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    8. Liao, Zengbu & Zhan, Keyi & Zhao, Hang & Deng, Yuntao & Geng, Jia & Chen, Xuefeng & Song, Zhiping, 2024. "Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    9. Wu, Zhangjun & Xu, Renli & Luo, Yuansheng & Shao, Haidong, 2024. "A holistic semi-supervised method for imbalanced fault diagnosis of rotational machinery with out-of-distribution samples," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    10. Yan, Shen & Zhong, Xiang & Shao, Haidong & Ming, Yuhang & Liu, Chao & Liu, Bin, 2023. "Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    11. Pang, Zhendong & Luan, Yingxin & Chen, Jiahong & Li, Teng, 2024. "ParInfoGPT: An LLM-based two-stage framework for reliability assessment of rotating machine under partial information," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    12. Keshun, You & Guangqi, Qiu & Yingkui, Gu, 2024. "Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Liu, Yi & Xiang, Hang & Jiang, Zhansi & Xiang, Jiawei, 2023. "Second-order transient-extracting S transform for fault feature extraction in rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    14. Dong, Yutong & Jiang, Hongkai & Wang, Xin & Mu, Mingzhe & Jiang, Wenxin, 2024. "An interpretable multiscale lifting wavelet contrast network for planetary gearbox fault diagnosis with small samples," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    15. Mandelli, Diego & Wang, Congjian & Agarwal, Vivek & Lin, Linyu & Manjunatha, Koushik A., 2024. "Reliability modeling in a predictive maintenance context: A margin-based approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    16. Dong, Yutong & Jiang, Hongkai & Yao, Renhe & Mu, Mingzhe & Yang, Qiao, 2024. "Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    17. Fu, Song & Zou, Limin & Wang, Yue & Lin, Lin & Lu, Yifan & Zhao, Minghang & Guo, Feng & Zhong, Shisheng, 2024. "DCSIAN: A novel deep cross-scale interactive attention network for fault diagnosis of aviation hydraulic pumps and generalizable applications," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    18. Cao, Yudong & Zhuang, Jichao & Miao, Qiuhua & Jia, Minping & Feng, Ke & Zhao, Xiaoli & Yan, Xiaoan & Ding, Peng, 2024. "Source-free domain adaptation for transferable remaining useful life prediction of machine considering source data absence," Reliability Engineering and System Safety, Elsevier, vol. 246(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:reensy:v:250:y:2024:i:c:s0951832024003144. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.