Machine Failure Prediction Using Machine Learning: A Multi-Stage Approach for Predictive Maintenance

The research here provides a predictive maintenance framework that uses machine learning todetect early failure in industrial equipment. A two-stage classification method is used, where the firstpredicts whether failure is imminent and the second predicts the exact type of failure. Feature engineeringmethods like power calculation and temperature difference analysis are used to improve the performanceof the model. Different machine learning models are tested, and their performance is compared withaccuracy, precision, recall, and F1-score. Model predictions are examined for interpretability andtransparency using an explainability method, which identifies important factors affecting failurepredictions. The approach boosts maintenance efficiency through the minimization of unplanneddowntime and maximization of maintenance scheduling, which leads to improved industrial reliability andcost reduction.