# An Affordable Fast Early Warning System for Edge Computing in Assembly Line
## 2 Related Studies
### Machine Learning for Fault Detection  
Technology developments in manufacturing generate new approaches to enhance product quality and prevent unexpected losses due to faults. Machine learning can analyze manufacturing data, providing managerial support as well as improving productivity. [PERSON] et al. proposed a method to detect ball bearing machine health status. Healthy vibration data was extracted using the Hilbert-Huang transform to track critical bearing component degradation. Then, the support vector machine (SVM) approach was used to detect faults [9]. Their results showed that the proposed method could effectively detect ball bearing faults. [PERSON] et al. proposed welding quality detection using SVM in a high-power disk layer [10]. Their proposed model successfully detected welding quality and could be implemented for real-time monitoring. Several machine learning models were utilized and evaluated to successfully detect metal casting quality [11]. Fault detection and analysis are important engineering problems to identify abnormal events during process. Early process fault detection and warning can help reduce or avoid productivity losses. Previous studies have identified that the RF machine learning model has good efficacy to detect manufacturing faults, e.g., failure detection of rotor bars [13], where RF achieved higher detection accuracy compared to other models and was suitable for in-process real-time fault detection. RF has also been used to detect bearing failure [14], outperforming neural network approaches in terms of performance and accuracy. Finally, RF was used to diagnose induction motor faults [12], achieving higher accuracy and faster execution compared to other models. Data preprocessing is a critical step to identify inconsistencies and/or outliers and generate better classification models. Previous studies have showed that eliminating outlier data can significantly improve classification accuracy [26; 27]. Clustering methods can be used for outlier detection based on the assumption that normal data correspond to dense clusters, whereas outlier data correspond to small groups, which are not included in any cluster [28]. The DBSCAN method distinguishes outlier data by finding dense regions based on the number of data close to a given point [15]. Points that do not belong to any cluster are regarded as outliers. DBSCAN is shown to provide excellent performance for distinguishing outlier from normal data, with consequentially improved classification accuracy. [PERSON] et al. utilized DBSCAN to detect outlier sensor data for a supply chain, successfully distinguishing outlier and normal data [29]. [PERSON] and [PERSON] proposed multiple parameter DBSCAN (DBSCAN-MP) to detect network intrusion, achieving a significantly higher detection rate compared to other methods [30]. [PERSON] and [PERSON] subsequently showed that the enhanced DBSCAN method could achieve higher intrusion detection accuracy compared to other methods [31]. Accuracy can also be improved by applying DBSCAN for outlier removal. [PERSON] et al. developed community detection utilizing DBSCAN to eliminate outliers and showed that the proposed method could precisely cluster community data for social network analysis [32]. [PERSON] et al. utilized DBSCAN for outlier removal to enhance diabetes and hypertension prediction accuracy [19]. Unbalanced class distribution is a common problem for supervised learning. Oversampling can be employed to address this problem, creating artificial data to balance the class distribution. The SMOTE oversampling method is widely used for machine learning, and several previous studies highlighted that this method subsequently improved model accuracy. [PERSON] et al. proposed a decision support system to automatically detect thin film transistor liquid crystal display (TFT-LCD) glass substrate defects using SVM [17], employing SMOTE techniques to balance their dataset. The results found that the accuracy of the model was increased.