Evaluation of predictive maintenance efficiency with the comparison of machine learning models in machining production process in brake industry

Challenges such as the imbalance of class distributions in the dataset, the presence of missing and outlier values, and the high costs of necessary equipment and training pose significant barriers to implementation. Addressing these issues is critical for achieving effective predictive maintenance solutions. In order to achieve more accurate results, data splitting and k-fold cross-validation methods were applied during the learning and testing phases to overcome the imbalance problem in the dataset, undersampling techniques were applied, and outlier detection and normalization processes were used to improve data quality. The model performances, evaluated through accuracy, precision, recall, and F1-score, area under the curve (AUC), Cohen's Matthew's correlation coefficient (MCC) were compared. Hyperparameter optimization was also performed, resulting in significant improvements in model performance. This study contributes to the literature in terms of predictive maintenance application, classification, and data partitioning techniques. The findings highlight the importance of data preprocessing and advanced modeling techniques in predictive maintenance and emphasize how addressing data challenges can enhance the overall performance and reliability of ML models.