现代信息科技2024，Vol.8Issue(10)：32-36,41,6.DOI:10.19850/j.cnki.2096-4706.2024.10.007
基于卷积神经网络的轴承剩余寿命预测方法
Prediction Method for Bearing Remaining Useful Life Based on Convolutional Neural Networks
摘要
Abstract
To improve the prediction accuracy and generalization ability of the Remaining Useful Life(RUL)prediction model for escalator bearings,a bearing RUL prediction method based on Convolutional Neural Network(CNN)is proposed.Firstly,it denoises the original data based on 3σ criterion,obtains its frequency characteristics through fast Fourier transformation.Secondly,it applies layered sampling different from traditional time series data partitioning methods to data partitioning,and constructs a Deep Convolutional Neural Network(DCNN)model consisting of three convolutional layers and two fully connected layers.Finally,the NASA IMS dataset is used to evaluate the preprocessing method,DCNN model accuracy,and generalization ability,proving the superiority of this method.关键词
剩余寿命预测/3σ准则/分层抽样/DCNN/泛化能力Key words
RUL prediction/3σ criterion/layered sampling/DCNN/generalization ability分类
信息技术与安全科学引用本文复制引用
张浩,赵军,王鹿,张银龙,程思宇..基于卷积神经网络的轴承剩余寿命预测方法[J].现代信息科技,2024,8(10):32-36,41,6.基金项目
南京地铁9号线一期工程基于物联网的自动扶梯故障预测与健康管理系统研究(JS-D.009.X-XY04-00-2005-0123) （JS-D.009.X-XY04-00-2005-0123）
铁四院科技研究开发计划项目(2022K053) （2022K053）
