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A New Approach for Remaining Useful Life Estimation Using Deep Learning

A New Approach for Remaining Useful Life Estimation Using Deep Learning Prognosis and Health Management (PHM) refer specifically to the prediction phase of the future behavior of the system or subsystem, including the remaining useful life (RUL). It is helpful to early detect incipient failures in many domains as aircraft, nuclear reactor, turbine gas, etc. In this paper we propose a new approach based on the implementation of data-driven methods for fault prognosis. Such methods require the availability of data describing the degradation process; when there is a lack of data, it is difficult to predict the states using deep models, which require a large amount of training data. In this paper, we propose to use a simple data augmentation strategy to solve the problem of data scarcity in prediction of RUL provided through the use of a long-short term memory (LSTM), which is a type of recurrent neural network. The results of our experiments demonstrate that using a simple data augmentation strategy can increase RUL prediction performance by using LSTM technics. We analyze our approach using data from NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

A New Approach for Remaining Useful Life Estimation Using Deep Learning

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References (24)

Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2023. ISSN 0146-4116, Automatic Control and Computer Sciences, 2023, Vol. 57, No. 1, pp. 93–102. © Allerton Press, Inc., 2023.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411623010030
Publisher site
See Article on Publisher Site

Abstract

Prognosis and Health Management (PHM) refer specifically to the prediction phase of the future behavior of the system or subsystem, including the remaining useful life (RUL). It is helpful to early detect incipient failures in many domains as aircraft, nuclear reactor, turbine gas, etc. In this paper we propose a new approach based on the implementation of data-driven methods for fault prognosis. Such methods require the availability of data describing the degradation process; when there is a lack of data, it is difficult to predict the states using deep models, which require a large amount of training data. In this paper, we propose to use a simple data augmentation strategy to solve the problem of data scarcity in prediction of RUL provided through the use of a long-short term memory (LSTM), which is a type of recurrent neural network. The results of our experiments demonstrate that using a simple data augmentation strategy can increase RUL prediction performance by using LSTM technics. We analyze our approach using data from NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS).

Journal

Automatic Control and Computer Sciences – Springer Journals

Published: Feb 1, 2023

Keywords: RUL; prognosis; deep learning; prediction

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