## Literature Review
### Machine Learning Methods in Manufacturing
The results showed that Random Forest had better performance and higher accuracy than the neural network algorithm. The results of this study are expected be used for bearing fault detection and diagnosis. Finally, [PERSON] et al. proposed fault diagnosis in spur gears based on genetic algorithm and Random Forest [49]. The proposed system consisted of two parts, namely genetic algorithm for attribute selection and Random Forest for classification. The proposed system was tested on real vibration signals and Random Forest had better performance for fault diagnosis. Machine learning algorithms encounter problems with outlier data, which can reduce the accuracy of the classification model. Outlier detection can be utilized in the preprocessing step to identify inconsistencies in data/outliers; thus, a good classifier can be generated for better decision making. Previous studies showed that removing the outlier can improve the classification accuracy. [PERSON] and [PERSON] utilized outlier detection for a classification model [50]. The authors proposed a statistical outlier detection method based on the interquartile range (IQR) with classes. The results showed that by removing the outliers from the training set, the classification performance of C4.5 was improved. [PERSON] et al. utilized an outlier prediction method to improve classification model performance in medical datasets [51]. The results showed that by removing the identified outliers from the training set, the classification accuracy was improved, especially for the Naive Bayes classifier. One of the techniques used for outlier detection is DBSCAN [52]. The algorithm works by identifying dense regions, which are determined based on the number of objects close to a given point. Finally, the algorithm identifies points that do not belong to any cluster, which are treated as outliers. DBSCAN has been implemented in different areas and showed significant accuracy by detecting true outliers. [PERSON] et al. proposed an outlier detection method involving soft sensor modeling of time series [53]. They utilized DBSCAN for outlier detection and the proposed outlier detection method demonstrated good performance. [PERSON] et al. proposed outlier detection based on DBSCAN for sensor data in wireless sensor networks [54]. The proposed model successfully separated outliers from normal sensor data. Based on experiments with synthetic datasets, the proposed model showed significant accuracy in detecting outliers, with an accuracy rate of 99%. Existing studies showed that Random Forest can be utilized for fault prediction with high classification accuracy. Furthermore, several studies showed significant results for DBSCAN-based outlier detection with regard to improving the classification accuracy. We propose a hybrid prediction model that consists of DBSCAN-based outlier detection to remove the outlier data, and Random Forest to detect whether the manufacturing process is functioning normally or abnormally. The hybrid prediction model is integrated with a real-time big data processing system, enabling processing of the sensor data from IoT-based sensor device (e.g., temperature, humidity, accelerometer, and gyroscope) and fault prediction in real-time.