IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v230y2023ics095183202200566x.html
   My bibliography  Save this article

A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold

Author

Listed:
  • Feng, Tingting
  • Li, Shichao
  • Guo, Liang
  • Gao, Hongli
  • Chen, Tao
  • Yu, Yaoxiang

Abstract

Accurate remaining useful life (RUL) prediction of the drill bit is central to ensuring the processing quality of products and enhancing processing safety and reliability. However, the majority of current methods are confined to considering the wear degradation during the drilling and ignore the combined effect of wear degradation and random shock on RUL. Therefore, it is difficult to the sudden failure prediction of the drill bit. To address this limitation, a degradation-shock dependent competing failure processes (DCFPs) based method considering time-shifting sudden failure threshold is proposed in this paper for RUL prediction. First, a degradation-shock DCFPs model is established based on the processing characteristics of the drill bit. Further, the expressions of reliability function and useful life taking into account the time-shifting sudden failure threshold are derived. Then, based on the Bayesian method, a two-step maximum likelihood estimation (MLE) is designed for parameter estimation, and the crucial parameters are updated utilizing a local gaussian importance resampling particle filter. Finally, a carbon composite laminate drilling experiment is provided for demonstration. Experimental results demonstrate the superiority of the proposed method for drill bit RUL prediction, especially when the drill bit is chipped.

Suggested Citation

  • Feng, Tingting & Li, Shichao & Guo, Liang & Gao, Hongli & Chen, Tao & Yu, Yaoxiang, 2023. "A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s095183202200566x
    DOI: 10.1016/j.ress.2022.108951
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183202200566X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108951?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. Che, Haiyang & Zeng, Shengkui & Guo, Jianbin & Wang, Yao, 2018. "Reliability modeling for dependent competing failure processes with mutually dependent degradation process and shock process," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 168-178.
    2. Wang, Han & Liao, Haitao & Ma, Xiaobing & Bao, Rui, 2021. "Remaining Useful Life Prediction and Optimal Maintenance Time Determination for a Single Unit Using Isotonic Regression and Gamma Process Model," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. Wen-An Yang & Wei Zhou & Wenhe Liao & Yu Guo, 2016. "Prediction of drill flank wear using ensemble of co-evolutionary particle swarm optimization based-selective neural network ensembles," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 343-361, April.
    4. Zhou, Taotao & Han, Te & Droguett, Enrique Lopez, 2022. "Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Yan, Tao & Lei, Yaguo & Li, Naipeng & Wang, Biao & Wang, Wenting, 2021. "Degradation modeling and remaining useful life prediction for dependent competing failure processes," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    6. Fan, Mengfei & Zeng, Zhiguo & Zio, Enrico & Kang, Rui, 2017. "Modeling dependent competing failure processes with degradation-shock dependence," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 422-430.
    7. Sun, Fuqiang & Li, Hao & Cheng, Yuanyuan & Liao, Haitao, 2021. "Reliability analysis for a system experiencing dependent degradation processes and random shocks based on a nonlinear Wiener process model," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    8. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    9. Wu, Bei & Ding, Dong, 2022. "A gamma process based model for systems subject to multiple dependent competing failure processes under Markovian environments," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. Zhao, Zeqi & Bin Liang, & Wang, Xueqian & Lu, Weining, 2017. "Remaining useful life prediction of aircraft engine based on degradation pattern learning," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 74-83.
    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. Xu, Dong & Jia, Xujie & Song, Xueying, 2024. "Reliability analysis of systems with n-stage shock process and m-stage degradation," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    2. Kang, Fengming & Cui, Lirong & Ye, Zhisheng & Zhou, Yu, 2024. "Reliability analysis for systems with self-healing mechanism in degradation-shock dependence processes with changing degradation rate," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Hou, WanJun & Peng, Yizhen, 2023. "Adaptive ensemble gaussian process regression-driven degradation prognosis with applications to bearing degradation," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Dui, Hongyan & Lu, Yaohui & Wu, Shaomin, 2024. "Competing risks-based resilience approach for multi-state systems under multiple shocks," Reliability Engineering and System Safety, Elsevier, vol. 242(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. Lyu, Hao & Qu, Hongchen & Yang, Zaiyou & Ma, Li & Lu, Bing & Pecht, Michael, 2023. "Reliability analysis of dependent competing failure processes with time-varying δ shock model," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Chen, Ying & Wang, Yanfang & Li, Shumin & Kang, Rui, 2023. "Hybrid uncertainty quantification of dependent competing failure process with chance theory," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Wei, Xiaohua & Bai, Sijun & Wu, Bei, 2023. "A novel shock-dependent preventive maintenance policy for degraded systems subject to dynamic environments and N-critical shocks," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Wu, Bei & Zhang, Yamei & Zhao, Songzheng, 2023. "Modeling coupled effects of dynamic environments and zoned shocks on systems under dependent failure processes," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. Wang, Jia & Meng, Bosen & Zhang, Luyu & Yu, Chengjiao, 2024. "Degradation modeling and reliability estimation for mechanical transmission mechanism considering the clearance between kinematic pairs," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    6. Wu, Bei & Wei, Xiaohua & Zhang, Yamei & Bai, Sijun, 2023. "Modeling dynamic environment effects on dependent failure processes with varying failure thresholds," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Liang, Qingzhu & Yang, Yinghao & Peng, Changhong, 2023. "A reliability model for systems subject to mutually dependent degradation processes and random shocks under dynamic environments," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Fang, Jiayue & Kang, Rui & Chen, Ying, 2021. "Reliability evaluation of non-repairable systems with failure mechanism trigger effect," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    9. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Nazarizadeh, Farzaneh & Alemtabriz, Akbar & Zandieh, Mostafa & Raad, Abbas, 2022. "An analytical model for reliability assessment of the rail system considering dependent failures (case study of Iranian railway)," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    11. Zhao, Chao & Shen, Weiming, 2022. "Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    12. Zhang, Wei & Wang, Ziwei & Li, Xiang, 2023. "Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Li, Xilin & Teng, Wei & Peng, Dikang & Ma, Tao & Wu, Xin & Liu, Yibing, 2023. "Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    14. Liu, Yao & Wang, Yashun & Fan, Zhengwei & Bai, Guanghan & Chen, Xun, 2021. "Reliability modeling and a statistical inference method of accelerated degradation testing with multiple stresses and dependent competing failure processes," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    15. Yousefi, Nooshin & Coit, David W. & Song, Sanling, 2020. "Reliability analysis of systems considering clusters of dependent degrading components," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    16. Cao, Shihao & Wang, Zhihua & Liu, Chengrui & Wu, Qiong & Li, Junxing & Ouyang, Xiangmin, 2023. "A novel solution for comprehensive competing failure process considering two-phase degradation and non-Poisson shock," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    17. Wu, Bei & Ding, Dong, 2022. "A gamma process based model for systems subject to multiple dependent competing failure processes under Markovian environments," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    18. Jiang, Shan & Jia, Xujie, 2024. "Reliability assessment under continuous fatigue degradation and shock based on Markov renewal process," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    19. Zhao, Zeyun & Wang, Jia & Tao, Qian & Li, Andong & Chen, Yiyang, 2024. "An unknown wafer surface defect detection approach based on Incremental Learning for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    20. Chen, Ying & Li, Shumin & Kang, Rui, 2021. "Epistemic uncertainty quantification via uncertainty theory in the reliability evaluation of a system with failure Trigger effect," Reliability Engineering and System Safety, Elsevier, vol. 215(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:230:y:2023:i:c:s095183202200566x. 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.