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Appl. Sci. 2019, 9(1), 84;

An Affordable Fast Early Warning System for Edge Computing in Assembly Line

Department of Industrial and Systems Engineering, Dongguk University, Seoul 100-715, Korea
u-SCM Research Center, Nano Information Technology Academy, Dongguk University, Seoul 100-715, Korea
Authors to whom correspondence should be addressed.
Received: 10 December 2018 / Revised: 20 December 2018 / Accepted: 23 December 2018 / Published: 26 December 2018
(This article belongs to the Special Issue Edge Computing Applications in IoT)
Full-Text   |   PDF [5058 KB, uploaded 26 December 2018]   |  


Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events. View Full-Text
Keywords: early warning system; sensors; edge computing; machine learning early warning system; sensors; edge computing; machine learning

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Syafrudin, M.; Fitriyani, N.L.; Alfian, G.; Rhee, J. An Affordable Fast Early Warning System for Edge Computing in Assembly Line. Appl. Sci. 2019, 9, 84.

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