# Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process
## 8 Conclusions
### Future Study  
For a smart factory, data analytics is not the only critical problem; data integration and real-time predictive analytics are also significant for PdM. In the future, we hope to develop an IIoT-based platform, which can be deployed on the edge- and fog-side in a manufacturing factory. Supervisory control, data acquisition, and real-time communication technology can be integrated with the trained model to enable efficient predictive maintenance in the future. In addition, users can leverage a graphical user interface to monitor manufacturing data and make efficient decisions based on predicted outcomes.  
Summarily, this study proposes using the proposed method to develop a predictive maintenance analytics mode, which can be deployed as an API and a WS. Few studies have investigated bagging and boosting ELAs simultaneously to address predictive maintenance in the manufacturing industry. In this study, we developed a new approach and investigated the decision jungle and boosted decision tree methods. The decision jungle ensemble method is based on the bagging ensemble-learning method. The boosted-based ensemble-learning method was used in the boosted decision tree module. We successfully used the proposed method and ensemble-learning module in the semiconductor manufacturing process and the blister packing machine process. The ensemble-learning module is a new type of ML method that can improve ML results by combining several training models instead of using a single training model. Our proposed algorithm is aimed at improving ensemble-learning performance by retraining the weaker pattern of an ensemble model. The experimental results showed that the proposed method outperformed the two types ofELAs, which achieved 97% accuracy. Some small- and medium-sized manufacturing firms may not be ready to migrate to IIoT- based PdM. Our proposed research framework may assist an emerging company or small- and medium-sized manufacturing firms in adopting smart manufacturing using data analytics technologies, as well as digital decision making. The use of data and APIs/WSs denotes a cost-efficient strategy rather than eliminating old equipment and purchasing advanced equipment. Such a framework is similar to the principle of Industry 3.5, which combines the practice of existing manufacturing for Industry 3.0 and future Industry 4.0 for smart manufacturing. Industry 3.5 technologies aim to develop an economic strategy using data, sensing elements, and networks. The small-scale manufacturing field can achieve the goal of smart manufacturing by keeping old equipment and adding nonintrusive elements to obtain and analyze data. Predictive analytics is an efficient way to add value to the manufacturing process, and it can be improved by adopting the principles of Industry 3.5 and 4.0.  
This research was funded by by Taiwan Ministry of Science and Technology (MOST), under Grant no. MOST 108-2410-H-224-001-MY2, MOST 109-2221-E-224-038, and MOST 108-2635-E-224 -001, MOST 110-2221-E-224 -047.  
The authors declare no conflict of interest.