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

作     者：Park, Donghyun Kim, Seulgi An, Yelin Jung, Jae-Yoon 

作者机构：Kyung Hee Univ Dept Ind & Management Syst Engn 1732 Deogyeong Daero Yongin 446701 South Korea 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2018年第18卷第7期

页      面：2110-2110页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：Smart Factory Advanced Technology Development Program of MOTIE/KEIT Advanced Training Program for Smart Factory of MOTIE/KIAT [N0002429] 

主　　题：data-driven fault detection prognostics and heath management edge computing real-time monitoring 

摘      要：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.