方位(导航)
滚动轴承
阶段(地层学)
过程(计算)
人工智能
贝叶斯概率
工程类
计算机科学
机器学习
数据挖掘
古生物学
物理
量子力学
振动
生物
操作系统
作者
Kaijian Chen,Jingna Liu,Wenwu Guo,Xizhao Wang
标识
DOI:10.1016/j.compeleceng.2023.108745
摘要
Remaining useful life (RUL) prediction of rolling element bearings is critical to maintaining rotating machinery and lowering industrial costs. There are many RUL prediction techniques, but most of them ignore two factors that may have a significant impact on prediction accuracy. One is the detection of the first predicting time (FPT) while the other is the predictive uncertainty. This paper proposes a two-stage approach to incorporating both factors into the prediction process based on Bayesian deep learning (BDL). In stage one, the state change of the bearing is identified and the FPT is determined according to a proposed detection technique. In stage two, RUL prediction is performed according to a new BDL model, and the results provide RUL point estimates and quantification of predictive uncertainty. The proposed two-stage approach has been validated on two publicly available bearing datasets, and the experimental results have demonstrated the effectiveness of the proposed approach in detecting FPT and its superiority over competitive BDL models.
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