Independently recurrent neural network for remaining useful life estimation  

作　　者：Wang Kaiye Cui Shaohua Xu Fangmin Zhao Chenglin 

机构地区：[1]School of Telecommunication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China [2]China Petroleum Technology Development Corporation,Beijing 100009,China [3]Information Technology Department,China Development Bank,Beijing 100031,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2020年第4期26-33,共8页中国邮电高校学报（英文版）

基　　金：supported by 2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of China “Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks”。

摘　　要：In the industrial fields, the mechanical equipment will inevitably wear out in the process of operation. With the accumulation of losses, the probability of equipment failure is increasing. Therefore, if the remaining useful life(RUL) of the equipment can be accurately predicted, the equipment can be maintained in time to avoid the downtime caused by equipment failure and greatly improve the production efficiency of enterprises. This paper aims to use independently recurrent neural network(IndRNN) to learn health degradation of turbofan engine and make accurate predictions of its RUL, which not only effectively solves the problem of gradient explosion and vanishing, but also increases the interpretability of neural networks. IndRNN can be used to process longer time series which matches the scene with high frequency sampling sensor in industrial practical applications. The results demonstrate that IndRNN for RUL estimation significantly outperforms traditional approaches, as well as convolutional neural network(CNN) and long short-term memory network(LSTM) for RUL estimation.

关 键 词：multivariate time series analysis independent recurrent neural network remaining useful life estimation prognostic and health management 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TH17[自动化与计算机技术—控制科学与工程]