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LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks

Department of Industrial and Management Systems Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si 446-701, Korea
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Sensors 2018, 18(7), 2110; https://doi.org/10.3390/s18072110
Received: 31 May 2018 / Revised: 27 June 2018 / Accepted: 28 June 2018 / Published: 30 June 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
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Abstract

Monitoring the status of the facilities and detecting any faults are considered an important technology in a smart factory. Although the faults of machine can be analyzed in real time using collected data, it requires a large amount of computing resources to handle the massive data. A cloud server can be used to analyze the collected data, but it is more efficient to adopt the edge computing concept that employs edge devices located close to the facilities. Edge devices can improve data processing and analysis speed and reduce network costs. In this paper, an edge device capable of collecting, processing, storing and analyzing data is constructed by using a single-board computer and a sensor. And, a fault detection model for machine is developed based on the long short-term memory (LSTM) recurrent neural networks. The proposed system called LiReD was implemented for an industrial robot manipulator and the LSTM-based fault detection model showed the best performance among six fault detection models. View Full-Text
Keywords: data-driven fault detection; prognostics and heath management; edge computing; real-time monitoring data-driven fault detection; prognostics and heath management; edge computing; real-time monitoring
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Park, D.; Kim, S.; An, Y.; Jung, J.-Y. LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks. Sensors 2018, 18, 2110.

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