Developing Predictive Maintenance for Early Warnings of Machine Failures Using Machine Learning Techniques in the Fourth Industrial Era

This study investigates the utilization of machine learning (ML) models in predictive maintenance for early warnings of probable machine failures. The main objective is to develop predictive maintenance models that can accurately predict prospective machine failures, requiring early warnings to assist proactive maintenance actions. Seven ML classifiers, including Gradient Boosting, SVM, Random Forest, XGBoost, LightGBM, CatBoost, AdaBoost, and ANN are assessed using different metrics. The findings demonstrate the effectiveness of the suggested approach with Gradient Boosting, realizing the highest accuracy of 0.9819. In addition, key indicators for predicting machinery failure in manufacturing are identified. The study highlights the importance of integrating machine learning into project management for accurate maintenance forecasts and learned decision-making.