降级(电信)
高斯过程
克里金
回归
计算机科学
高斯分布
过程(计算)
人工智能
机器学习
统计
数学
电信
物理
量子力学
操作系统
作者
Wensheng Hou,Yizhen Peng
标识
DOI:10.1016/j.ress.2023.109479
摘要
Degradation modeling and remaining useful life prediction of bearings is crucial for predictive maintenance of rotating machinery. However, the contradiction between limited full-life cycle samples and dynamically diverse degradation trends has become the main obstacle for degradation modeling and prediction. To address these challenges, this paper proposes an adaptive time-varying ensemble Gaussian process regression-driven degradation prediction method. Firstly, four different base predictors (i.e., global predictor, healthy stage predictor, impending degradation stage predictor and degradation stage predictor) are constructed based on Gaussian regression process to reflect the characteristics of different degradation stages. On this basis, a time-varying ensemble learning method with adaptive weights is proposed, and a corresponding adaptive ensemble Gaussian regression process is constructed to model the full-life degradation process. The model can effectively enhance the flexibility and prediction accuracy of the single-time invariant Gaussian regression model. Some real bearing degradation cases are used to validate the proposed method.
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