IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v242y2024ics0951832023006828.html
   My bibliography  Save this article

A comprehensive study on developing an intelligent framework for identification and quantitative evaluation of the bearing defect size

Author

Listed:
  • Kumar, Anil
  • Kumar, Rajesh
  • Tang, Hesheng
  • Xiang, Jiawei

Abstract

An intelligent framework is necessary to detect and analyze bearing defects in rotating machinery to prevent unexpected downtime and achieve performance per Industry 4.0 standards. This study presents a framework to detect faults and precisely quantify their size. The framework triggers an AI model to identify the defect and another model for quantitative evaluation of defect size. After analyzing the various classification and regression models, it has been found that The k-nearest neighbor (KNN) algorithm is suggested as the most effective AI model for identifying bearing defects. The ensemble tree is the most effective AI model for defect quantification. The results showed that the proposed algorithm can estimate the defect width reasonably. The maximum error in estimating the inner race, outer race, and roller defect widths was 2.474%, 14.534%, and 5.517%, respectively. The AI model's capacity to identify bearing defects of different sizes, which were not included in the training dataset, was also tested. The test yielded successful results. By using this framework, industries can prevent unexpected downtime, reduce maintenance costs, and enhance the performance and reliability of rotating machinery.

Suggested Citation

  • Kumar, Anil & Kumar, Rajesh & Tang, Hesheng & Xiang, Jiawei, 2024. "A comprehensive study on developing an intelligent framework for identification and quantitative evaluation of the bearing defect size," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s0951832023006828
    DOI: 10.1016/j.ress.2023.109768
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023006828
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109768?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. Oh, YeongGwang & Ransikarbum, Kasin & Busogi, Moise & Kwon, Daeil & Kim, Namhun, 2019. "Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 202-212.
    2. Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Ahmad, Wasim & Khan, Sheraz Ali & Islam, M M Manjurul & Kim, Jong-Myon, 2019. "A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 67-76.
    4. Veiga, Sébastien Da & Marrel, Amandine, 2020. "Gaussian process regression with linear inequality constraints," Reliability Engineering and System Safety, Elsevier, vol. 195(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. López-Lopera, Andrés F. & Idier, Déborah & Rohmer, Jérémy & Bachoc, François, 2022. "Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    2. Wu, Jingyao & Zhao, Zhibin & Sun, Chuang & Yan, Ruqiang & Chen, Xuefeng, 2021. "Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Palar, Pramudita Satria & Zuhal, Lavi Rizki & Shimoyama, Koji, 2023. "Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    4. Ding, Zhikun & Xu, Shengqu & Xie, Xiaofeng & Zheng, Kairui & Wang, Daochu & Fan, Jianhao & Li, Hong & Liao, Longhui, 2024. "A building information modeling-based fire emergency evacuation simulation system for large infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Wang, Han & Wang, Dongdong & Liu, Haoxiang & Tang, Gang, 2022. "A predictive sliding local outlier correction method with adaptive state change rate determining for bearing remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Qu, Pengfei & Zhang, Limao & Zhu, Qizhi & Wu, Maozhi, 2023. "Probabilistic reliability assessment of twin tunnels considering fluid–solid coupling with physics-guided machine learning," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. Liu, Junqiang & Yu, Zhuoqian & Zuo, Hongfu & Fu, Rongchunxue & Feng, Xiaonan, 2022. "Multi-stage residual life prediction of aero-engine based on real-time clustering and combined prediction model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Ma, Chenyang & Li, Yongbo & Wang, Xianzhi & Cai, Zhiqiang, 2023. "Early fault diagnosis of rotating machinery based on composite zoom permutation entropy," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Zeki Murat Çınar & Abubakar Abdussalam Nuhu & Qasim Zeeshan & Orhan Korhan & Mohammed Asmael & Babak Safaei, 2020. "Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0," Sustainability, MDPI, vol. 12(19), pages 1-42, October.
    10. Lee, Jinwook & Kim, Myungyon & Ko, Jin Uk & Jung, Joon Ha & Sun, Kyung Ho & Youn, Byeng D., 2022. "Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    11. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    12. Wang, Ning & Zhao, Shiyue & Wang, Sutong, 2024. "A novel clustering-based resampling with cost-sensitive boosting method to model and map wildfire susceptibility," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    13. Wang, Zhijie & Zhai, Qingqing & Chen, Piao, 2021. "Degradation modeling considering unit-to-unit heterogeneity-A general model and comparative study," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang, 2022. "The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    15. Wang, Yuhao & Gao, Yi & Liu, Yongming & Ghosh, Sayan & Subber, Waad & Pandita, Piyush & Wang, Liping, 2021. "Bayesian-entropy gaussian process for constrained metamodeling," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    16. Saeed, Umer & Jan, Sana Ullah & Lee, Young-Doo & Koo, Insoo, 2021. "Fault diagnosis based on extremely randomized trees in wireless sensor networks," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    17. Bermeo-Ayerbe, Miguel Angel & Cocquempot, Vincent & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2023. "Remaining useful life estimation of ball-bearings based on motor current signature analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    18. Sun, Bin & Li, Yan & Zhang, Yangyang & Guo, Tong, 2024. "Multi-source heterogeneous data fusion prediction technique for the utility tunnel fire detection," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    19. Mariusz Zieja & Andrzej Gębura & Andrzej Szelmanowski & Bartłomiej Główczyk, 2021. "Non-Invasive Monitoring of the Technical Condition of Power Units Using the FAM-C and FDM-A Electrical Methods," Sustainability, MDPI, vol. 13(23), pages 1-19, December.
    20. Li, Guofa & Wei, Jingfeng & He, Jialong & Yang, Haiji & Meng, Fanning, 2023. "Implicit Kalman filtering method for remaining useful life prediction of rolling bearing with adaptive detection of degradation stage transition point," Reliability Engineering and System Safety, Elsevier, vol. 235(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:242:y:2024:i:c:s0951832023006828. 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.