Machine Learning-Based Predictive Maintenance and Fault Diagnosis for Intelligent Mechanical Systems

Abstract

Predictive maintenance has become a critical component of Industry 4.0 by enabling intelligent mechanical systems to minimize unexpected equipment failures, reduce maintenance costs, and improve operational reliability. This study proposes a machine learning-based predictive maintenance framework for machine failure prediction and fault diagnosis using the AI4I 2020 Predictive Maintenance Dataset. The dataset comprises 10,000 machine observations with operational parameters, including air temperature, process temperature, rotational speed, torque, tool wear, and machine type. A comprehensive comparative analysis was conducted using Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost). To address the highly imbalanced failure distribution, multiple evaluation metrics, including Accuracy, Precision, Recall, F1-score, Receiver Operating Characteristic–Area Under the Curve (ROC–AUC), and Matthews Correlation Coefficient (MCC), were employed. The experimental results demonstrated that XGBoost achieved the best predictive performance, obtaining an accuracy of 98.95%, an F1-score of 82.93%, a ROC–AUC of 98.00%, and an MCC of 0.8289, outperforming the remaining machine learning models. Furthermore, SHapley Additive exPlanations (SHAP) identified torque, torque–speed ratio, and tool wear as the most influential predictors of machine failure, providing transparent model interpretation and supporting informed maintenance decision-making. The proposed framework offers an accurate and interpretable solution for predictive maintenance, contributing to enhanced equipment reliability, reduced downtime, and intelligent maintenance strategies in modern manufacturing environments.