预言
计算机科学
序列(生物学)
滑动窗口协议
比例(比率)
过程(计算)
期限(时间)
集合(抽象数据类型)
数据挖掘
短时记忆
数据集
持续时间(音乐)
人工智能
可靠性工程
窗口(计算)
机器学习
人工神经网络
工程类
循环神经网络
量子力学
艺术
文学类
程序设计语言
生物
遗传学
物理
操作系统
作者
Ruiguan Lin,Yaowei Yu,Huawei Wang,Changchang Che,Xiaomei Ni
标识
DOI:10.1016/j.jocs.2021.101508
摘要
The development of sensors and artificial intelligence technology provides practical tools for aircraft Prognosis and Health Management (PHM). The remaining useful life (RUL) prediction is the critical process of PHM. A novel data-driven framework is proposed to estimate the RUL of complex systems in this paper. The framework evaluates the system's RUL based on multi-scale sequences and Long Short-Term Memory (LSTM) networks. First, the sliding time window method is used to prepare training samples, and the degradation features are directly mapped to RUL predictions. In addition, the model parameters are adjusted through the input multi-scale sequence to obtain the best prediction performance. This method integrates the application of time window, multi-scale sequence, and LSTM structure to improve prediction accuracy. The proposed method is validated using the NASA C-MAPSS data set, and the results demonstrate the superiority of the proposed framework.
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