计算机应用与软件2024，Vol.41Issue(8)：60-66,7.DOI:10.3969/j.issn.1000-386x.2024.08.009
基于深度神经网络的设备剩余使用寿命预测研究
REMAINING USEFUL LIFE ESTIMATION BASED ON DEEP NEURAL NETWORKS
摘要
Abstract
With the broad deployment of sensors in industrial equipment,data-driven device state prognostics and health management have received increasing attention from both academia and industry.This paper focused on prognostics of systems'remaining useful life(RUL).Deep neural networks to build the key step of RUL estimation models.We evaluated the RUL estimation performance of the models using three typical deep neural networks,namely,feed-forward neural network(FNN),convolution neural network(CNN),and long and short-term memory(LSTM),based on a benchmark dataset C-MAPSS.The experimental results demonstrate that LSTM considering temporal features have significant performance advantages.The research trends in RUL prediction are discussed.关键词
剩余使用寿命预测/深度神经网络/卷积神经网络/长短期记忆网络Key words
Remaining useful life estimation/Deep neural networks/Convolution neural network/Long and short-term memory分类
信息技术与安全科学引用本文复制引用
王加昌,赖跖,唐雷,田野,刘梦娟..基于深度神经网络的设备剩余使用寿命预测研究[J].计算机应用与软件,2024,41(8):60-66,7.基金项目
国家自然科学基金项目(61202445). （61202445）
