预言
线性化
预处理器
降级(电信)
数据挖掘
可靠性工程
转化(遗传学)
计算机科学
非线性系统
工程类
人工智能
电信
生物化学
化学
物理
量子力学
基因
作者
Jie Liu,Bingchang Hou,Ming Lu,Dong Wang
标识
DOI:10.1016/j.ress.2024.109952
摘要
Remaining useful life (RUL) prediction is a core of prognostics and health management (PHM), which is beneficial to arranging preventive maintenance and avoiding sudden equipment breakdown. Accurate modeling of various degradation data and appropriate data preprocessing are important prerequisites for precise RUL prediction. Practically, nonlinear degradation data will increase the difficulty of degradation modeling, thereby affecting RUL prediction accuracy. In currently existing research, data linearization using Box-Cox transformation (BCT) is often treated as a separate data preprocessing step and its integration with prognostic modeling is seldom considered and reported. This paper considers the integration of BCT with state-space modeling as a unified prognostic framework to effectively linearize degradation data and thus simplify degradation modeling and enhance RUL prediction accuracy. The proposed unified prognostic framework is validated by using both simulated data and experimental gearbox degradation data. Experimental results demonstrate that the proposed unified prognostic framework exhibits excellent performance in degradation data linearization and RUL prediction enhancement. Moreover, comparisons with a recently advanced prognostic method based on BCT and Bayesian model parameters updating demonstrate the superiority of the integration of BCT with state-space modeling for achieving higher RUL prediction accuracy and less prediction errors.
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