A degradation-shock dependent competing failure processes based method for remaining useful life prediction of drill bit considering time-shifting sudden failure threshold. (February 2023)

• Record Type: Journal Article
• Authors: Feng, Tingting; Li, Shichao; Guo, Liang; Gao, Hongli; Chen, Tao; Yu, Yaoxiang
• Is Part Of: Reliability engineering & system safety. Volume 230(2023)
• Publication Date: 2023-02
• Subjects: Remaining useful life prediction -- Drill bit -- Dependent competing failure processes -- Sudden failure; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108951
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A dependent competing failure model is established for drill lifetime estimation. The effect of wear degradation on the sudden failure threshold is considered. Reliability functions and useful life expression of the drill are derived. A drilling experiment is provided for validating the proposed method. 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.