# Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process
## 5 Experiment
### Case Data Description  
We used two datasets as case studies to demonstrate how the proposed method uses data analytics for anomaly prediction. Here, the manufacturing case aimed to predict yield failure in the semiconductor manufacturing process.This study aims to address the requirements of a practical manufacturing factory. Hence, we selected a conventional product packaging company as another case. The case is a typical small-sized manufacturing enterprise in Asia; they have a standard operating procedure and conventional production facilities to package different types of scissors and other cutting tools. The case used the blister packing machine to package finished goods. The customized scissor product posed a challenge to completing the packaging process, which could be caused by faulty packaging and an incomplete seal as a packaging component (see Figure 6). Finished goods with faulty packaging are deemed unqualified products. Small-sized manufacturing enterprises may not be ready for Industry 4.0 migration and face the challenge of insufficient funds (e.g., funds for purchasing advanced equipment). Similar to other small-sized manufacturing enterprises, the case at improving the quality of current manufacturing processes herein was performed using an economic means rather than purchasing new intelligent equipment to achieve Industry 4.0. Therefore, this study improves the manufacturing process using artificial intelligence, which only requires current equipment data to improve smart manufacturing. In this case, we predicted the quality of scissor product packaging using a blister packing machine setting to correctly seal package components.  
Figure 5: Procedure of industrial data analytics from import to deployment.  
Two case data sources were used for data analysis to address the PdM problem in the manufacturing industry. Herein, the UCI ML repository served as one of the data sources. The prediction target is a binary outcome of the yield status (e.g., pass or fail). The values of 0 and 1 represent a pass and fail in the test point, respectively. The manufacturing case is a predictive case that aims to predict yield failure in the semiconductor manufacturing process. The semiconductor dataset comprises 1567 instances with 591 features. The number of anomaly instances and normal instances are 104 instances and 1463 instances, respectively. The proportion of anomaly data is 6.6% in the total dataset, and the proportion of semiconductor dataset was biased toward the normal class. The other data source is the blister packing machine dataset. The main attribute of this dataset is related to equipment settings, including the heat-sealing time, cooling time, material of the blister bottom cover, and material of the cards. Table 2 describes the above-described datasets. Semiconductor data contain 17,836 instances with six attributes. The number of anomaly instances and normal instances are 17,464 instances and 372 instances, respectively. The proportion of blister packing machine dataset was biased toward the anomaly class (unqualified products), and normal data constitute 2.1% of the total dataset. Both manufacturing datasets faced the data imbalance problem. The anomaly data with the lowest or highest number of instances is usually the class of interest from the perspective of PdM. We used the SMOTE algorithm to address this problem by generating synthetic samples through a combination of existing samples for increasing the number of minority samples to be equal to the other class samples. Both preprocessed datasets had the same the number of instances between anomaly data and normal data after using the SMOTE algorithm to enlarge the dataset.  
Figure 6: A case study in a practical manufacturing company.