# An Affordable Fast Early Warning System for Edge Computing in Assembly Line
## 4 Results and Discussions
### Managerial Implications  
There are three main management implications for the proposed AFEWS: Low-cost system development, assembly line monitoring, and improved decision-making due to timely fault warnings. The study utilized edge computing technology based on a Raspberry Pi SBC that offered small size, low cost, and sufficient processing power [54; 55]. The edge device also included Sense Hat, DHT11, BH1750 FVI, and ZP01-MP503 sensors, and provided a 16 GB micro SD memory card for local database storage of sensor and fault prediction data. The total cost for each edge device was approximately $91 [56; 57; 58; 59; 60; 61], although prices may vary from different suppliers for each component. Therefore, the edge devices were considered to be cost effective.  
Previous studies identified significant advantages to utilizing Raspberry Pi for similar scenarios, including secure and robust healthcare services [62], intelligent video surveillance platforms [63], building management systems [64], and beehive monitoring [65]. Edge computing benefits have been clarified by several studies to include reduced response time [66] and energy consumption [67].  
Figure 10: DBSCAN and SMOTE impacts for classification accuracy. NB = Naïve Bayes, LR = logistic regression, MLP = multilayer perceptron, DT = decision tree, RF = random forest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Performance Metric (\%)**} \\ \hline
**Model** & **r** & **f** & **P** & **a** \\ \hline Naive Bayes & 68.53 & 67.02 & 67.54 & 67.43 \\ Logistics regression & 81.15 & 82.02 & 83.65 & 83.55 \\ Multilayer perceptron & 94.6 & 94.81 & 95.05 & 95.11 \\ Decision tree & 94.55 & 94.65 & 94.76 & 94.95 \\ Random forest & 96.85 & 96.74 & 96.63 & 96.91 \\ Proposed AFEWS & 98.65 & 98.64 & 98.63 & 98.64 \\ \hline \hline \end{tabular}  
Notes: r = recall, f = F-1 score, p = precision, and a = accuracy.  
\end{table}
Table 5: Performance comparison of several classification models.  
Adopting OSS can also generate economic gains, including software development productivity, low investment cost (i.e., licenses), and external support availability [68; 69; 70].  
Machine learning has been applied to manufacturing to differentiate carbon fiber fabric type [71], diagnose locomotion gait faults for reconfigurable robots [72], machine tool health status [73], early stage electrical fault detection in induction motors [74], sealing surface defect for chili oil production line [75], metallic surfaces for a flat metal component production line [76], aerospace deburring prediction [77], remaining turbofan engine useful life [78], etc. The present study adopted a machine learning model in the edge devices to detect fault events during assembly line process. Once a fault was detected, the proposed AFEWS sends a warning message to registered users via email and/or IM. Thus, managers can notice faults immediately and take action to prevent further losses. Results from the present study provide practical guidelines for industrial practitioners to develop edge devices and machine learning models for early warning systems into their manufacturing process.