首页 | 本学科首页   官方微博 | 高级检索  
     

基于LSTM循环神经网络的故障时间序列预测
引用本文:王鑫,吴际,刘超,杨海燕,杜艳丽,牛文生. 基于LSTM循环神经网络的故障时间序列预测[J]. 北京航空航天大学学报, 2018, 44(4): 772-784. DOI: 10.13700/j.bh.1001-5965.2017.0285
作者姓名:王鑫  吴际  刘超  杨海燕  杜艳丽  牛文生
作者单位:1.北京航空航天大学计算机学院, 北京 100083
基金项目:中国民用航空专项研究项目(MJ-S-2013-10),国防科工局技术基础项目(JSZL2014601B008),国家自然科学基金(61602237) China Civil Aviation Special Research Project(MJ-S-2013-10),Technology Foundation Program of the National Defense Technology Industry Ministry(JSZL2014601B008),National Natural Science Foundation of China(61602237)
摘    要:有效地预测使用阶段的故障数据对于合理制定可靠性计划以及开展可靠性维护活动等具有重要的指导意义。从复杂系统的历史故障数据出发,提出了一种基于长短期记忆(LSTM)循环神经网络的故障时间序列预测方法,包括网络结构设计、网络训练和预测过程实现算法等,进一步以预测误差最小为目标,提出了一种基于多层网格搜索的LSTM预测模型参数优选算法,通过与多种典型时间序列预测模型的实验对比,验证了所提出的LSTM预测模型及其参数优选算法在故障时间序列分析中具有很强的适用性和更高的准确性。 

关 键 词:长短期记忆(LSTM)模型   循环神经网络   故障时间序列预测   多层网格搜索   深度学习
收稿时间:2017-05-08

Exploring LSTM based recurrent neural network for failure time series prediction
WANG Xin,WU Ji,LIU Chao,YANG Haiyan,DU Yanli,NIU Wensheng. Exploring LSTM based recurrent neural network for failure time series prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 772-784. DOI: 10.13700/j.bh.1001-5965.2017.0285
Authors:WANG Xin  WU Ji  LIU Chao  YANG Haiyan  DU Yanli  NIU Wensheng
Affiliation:1.School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China2.Fengtai Vocational Education Central School, Beijing 100076, China3.Aeronautical Computing Technique Research Institute, Aviation Industry Corporation of China, Xi'an 710068, China
Abstract:Effectively forecasting the failure data in the usage stage is essential to reasonably make relia-bility plans and carry out reliability maintaining activities.Beginning with the historical failure data of complex system,a long short-term memory(LSTM)based recurrent neural network for failure time series prediction is presented,in which the design of network structure, the procedures and algorithms of network training and forecasting are involved.Furthermore,a multilayer grid search algorithm is proposed to optimize the parame-ters of LSTM prediction model.The experimental results are compared with various typical time series predic-tion models,and validate that the proposed LSTM prediction model and the corresponding parameter optimiza-tion algorithm have strong adaptiveness and higher accuracy in failure time series prediction.
Keywords:long short-term memory (LSTM) model  recurrent neural network  failure time series prediction  multilayer grid search  deep learning
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《北京航空航天大学学报》浏览原始摘要信息
点击此处可从《北京航空航天大学学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号