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Independently recurrent neural network for remaining useful life estimation

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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.

multivariate time series analysisindependent recurrent neural networkremaining useful life estimationprognostic and health management

Wang Kaiye、Cui Shaohua、Xu Fangmin、Zhao Chenglin

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School of Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Information Technology Department, China Development Bank, Beijing 100031, China

China Petroleum Technology Development Corporation, Beijing 100009, China

2019 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of China

2020

10.19682/j.cnki.1005-8885.2020.0034

中国邮电高校学报(英文版)
北京邮电大学

中国邮电高校学报(英文版)

CSCDEI
影响因子：0.419
ISSN：1005-8885
年,卷(期)：2020.27(4)
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