# An Affordable Fast Early Warning System for Edge Computing in Assembly Line
## 1 Introduction  
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]. A transition from traditional to advanced manufacturing can be enabled by adopting ICT [3]. Early warning systems within ICT applications can provide important inputs to manufacturing processes and management, and integrating ICT with IoT and sensors enables early warning systems to monitor manufacturing processes. With recent cloud computing developments, sufficient computing and storage resources can be acquired without requiring physical data centers or servers [4, 5]. However, cloud computing has a number of drawbacks, including low response time due to centralized computation, where the data must first be transmitted to the cloud for computation and the result returned later. On the other hand, short response time and real-time decision support are essential for some IoT applications, such smart healthcare [6], emergency response [7], and early warning systems [8]. Edge computing can be utilized to overcome these cloud issues by providing computation and storage resources near the data source, minimizing latency, and providing real-time decision-making. Manufacturing data, e.g., sensor data, process logs, etc., must be analyzed to facilitate meaningful decision-making. Machine learning has great potential to analyze data and has been successfullyapplied for quality [9; 10; 11] and fault detection [12; 13; 14]. For example, random forest (RF) can provide high accuracy for detecting abnormal events in a process, whereas traditional machine learning models encounter challenging issues, such as outlier data and imbalanced datasets, with consequentially low model accuracy. Several studies have demonstrated that eliminating outlier data using density based spatial clustering of applications with noise (DBSCAN) methods [15] and balancing the dataset using synthetic minority over sampling techniques (SMOTE) [16] can significantly improve model accuracy [17; 18; 19]. 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. The model comprised of DBSCAN based outlier detection, SMOTE, and the RF algorithm. DBSCAN was employed to eliminate outlier data, SMOTE to balance the dataset, and RF to predict faults. We developed a web dashboard to visualize sensor data and fault status in fast response. The proposed system was implemented in a Korean automobile parts factory. Contributions from the present study can be summarized as follows:  
* _Affordable edge computing system._ The proposed system employed an edge device based on an open source single-board computer (SBC), providing low cost, good support, and sufficient computation resources. 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, thus minimizing network latency and improving analysis speed. * _Fault detection based on hybrid fault model._ We utilized a hybrid fault model combining DBSCAN outlier detection, SMOTE, and RF to improve prediction accuracy. The hybrid fault model learned and generated the model that was subsequently used in the edge device to predict faults in fast response. * _Implementation and performance analysis._ The proposed system was tested in the door trim assembly line for a Korean automobile parts factory. The selected edge device provided sufficient performance, successfully gathering, analyzing, and displaying sensor data in fast response.