Machine Learning-Based Predictive Maintenance for Industrial Machinery

Abstract

Industrial machinery reliability plays a critical role in ensuring production continuity, cost efficiency, and operational safety in modern manufacturing systems. Unplanned equipment failures may cause significant downtime, financial losses, and safety risks. Traditional maintenance strategies, such as reactive and preventive maintenance, often fail to consider the actual health condition of machines, leading either to unexpected breakdowns or unnecessary maintenance actions. Therefore, intelligent data-driven approaches are required to predict failures before they occur. This study aimed to develop and evaluate a machine learning–based predictive maintenance framework using operational sensor data from industrial machinery. A quantitative experimental design was employed using 15,000 multivariate sensor records consisting of vibration, temperature, pressure, rotational speed, and load measurements. Data preprocessing included outlier screening, normalization, and statistical feature extraction. The dataset was divided into training and testing sets using stratified sampling. Three supervised learning models Random Forest, Support Vector Machine, and Artificial Neural Network were implemented and optimized using cross-validation. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The results showed that all models achieved classification accuracy above 90 percent. Random Forest demonstrated the highest performance, achieving 94.2 percent accuracy and 95.1 percent recall, along with the lowest number of false negatives and stable cross-validation results. Feature importance analysis indicated that vibration and temperature were the most influential predictors of failure conditions. The findings confirm that machine learning techniques can effectively support predictive maintenance using structured sensor data. The proposed framework contributes to the development of reliable and practical data-driven maintenance strategies in industrial environments. Future research should explore deep learning methods and multi-machine validation to enhance generalizability and prognostic capabilities