@article{article_1571811, title={Failure Prediction Using Ensemble Learning: A Comparative Study with Synthetic and Real-World Datasets}, journal={Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi}, volume={25}, pages={785–797}, year={2025}, DOI={10.35414/akufemubid.1571811}, author={Çiftpınar, Aslı Beyza and Kanar, Pelin and Erzurum Cıcek, Zeynep Idil}, keywords={Arıza tahmini, Makine öğrenmesi, Topluluk öğrenmesi, Aşırı örnekleme}, abstract={The ability to predict and prevent machine failures is a crucial task for businesses on a global scale at a time of increasing dependence on automation and technology. This paper primarily addressed a novel failure prediction model approach based on ensemble learning. Commonly used machine learning models including Decision Trees, K-Nearest Neighborhood, Support Vector Machines, and Logistic Regression and two different ensemble learning strategies were used: bagging and majority voting. The SZVAV real-life failure dataset provided by Lawrence Berkeley National Laboratory and the AI4I2020 Predictive Maintenance synthetic dataset were utilized to evaluate the performance of the proposed ensemble models. The preprocessing stage included the application of oversampling since there is an imbalance problem in both datasets. In this context, a comparison of three oversampling techniques was also presented for the datasets considered in the study. As a result of the tests, it was seen that the proposed models are superior to individual machine learning methods and Random Forest, which is an ensemble model itself, for the considered datasets. In addition, the proposed ensemble models were compared with the original failure prediction models previously presented in the literature on the AI4I2020 dataset, and it was reported that more successful results are obtained with the proposed approach.}, number={4}, publisher={Afyon Kocatepe Üniversitesi}, organization={Eskişehir Teknik Üniversitesi}