Abstract
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.
Highlights
The concept of Industry 4.0 [1] was recently proposed as the new state of the art between information and communication technology (ICT) and manufacturing technologies, offering opportunities to significantly enhance manufacturing systems and help improve product quality, production efficacy, and allow real-time condition monitoring and decision-making [2]
We proposed affordable fast early warning system (AFEWS) based on edge devices and hybrid fault model to identify faults during process in fast response
The edge device incorporated several sensor devices and collected and processed sensor data to be subsequently analyzed on the edge device using a hybrid fault model
Summary
The concept of Industry 4.0 [1] was recently proposed as the new state of the art between information and communication technology (ICT) and manufacturing technologies, offering opportunities to significantly enhance manufacturing systems and help improve product quality, production efficacy, and allow real-time condition monitoring and decision-making [2]. The present study proposed an affordable fast early warning system (AFEWS) utilizing edge devices and a hybrid fault model. The edge device was a computation unit close to the data source (sensors), and we used a hybrid fault model to predict whether the process was functioning normally or abnormally. Sensor devices were combined with the SBC to gather, process, analyze, and present the sensor data and consequential results in a web dashboard without requiring network communication to the cloud server, minimizing network latency and improving analysis speed. We utilized a hybrid fault model combining DBSCAN outlier detection, SMOTE, and RF to improve prediction accuracy. The selected edge device provided sufficient performance, successfully gathering, analyzing, and displaying sensor data in fast response.
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