Machine learning-based predictive maintenance: enhancing industrial reliability through data-driven approaches

This study investigated the application of machine learning for predictive maintenance (PM) using synthetic data simulating industrial ma-‎chinery failures. Different algorithms including random forest, support vector machine (SVM), artificial neural network (ANN), decision ‎tree (DT), and logistic regression (LR) were evaluated in two test scenarios. Decision tree (DT) and logistic regression (LR) showed the ‎best promise, despite challenges with data imbalance and data segmentation. However, these models are not yet suitable for industrial de-‎ployment due to the significant impact of misclassified faults. The results highlight the potential of machine learning to improve predictive ‎maintenance (PM), while further improvements are needed before it can replace human supervision‎.