Paper Info
Title
LiRUL: A Lightweight LSTM Based Model for Remaining Useful Life Estimation at the Edge
Abstract
Unexpected downtime and sudden breakdown of IoT devices can be extremely destructive most especially for safety-critical systems. Condition monitoring and health state estimation are vital techniques for maintaining high reliability and availability. Many data-driven approaches to Remaining Useful Life (RUL) estimation using deep learning algorithms are computationally-intensive and often not feasible on resource-constrained devices. Time lag and network cost associated with massive data transfer to a centralized cloud for processing can be minimized by adopting edge computing. In this paper, we propose a lightweight Long Short-Term Memory (LSTM) based model called LiRUL, suitable for RUL estimation on an edge device. We also implement a tagging function and evaluate our approach on publicly available Turbofan Engine datasets.
Year
DOI
Venue
2019
10.1109/COMPSAC.2019.10203
2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)
Keywords
DocType
Volume
IoT,RUL Estimation,Edge Computing,LSTM
Conference
2
ISSN
ISBN
Citations 
0730-3157
978-1-7281-2607-4
0
PageRank 
References 
Authors
0.34
6
2
Name
Order
Citations
PageRank
Olumide Kayode111.37
Ali Şaman Tosun219010.03