Machine Learning-Based Predictive Maintenance for Industrial Rotating Equipment

Abstract

This paper proposes a predictive maintenance framework based on machine learning techniques for industrial rotating equipment. Vibration and temperature signals were collected to train classification models capable of identifying potential faults. Experimental results demonstrate high prediction accuracy and improved maintenance scheduling. The proposed method reduces unexpected downtime and maintenance costs while enhancing equipment reliability and operational efficiency in industrial manufacturing systems.