Development of Predictive Maintenance Models Using Machine Learning for Manufacturing Equipment to Reduce Operational Downtime

Abstract

Unplanned equipment failures in manufacturing environments lead to significant production losses, increased maintenance costs, and safety risks. Traditional maintenance strategies, including reactive and preventive maintenance, often fail to detect early-stage faults or result in unnecessary servicing. Predictive maintenance (PdM) addresses these limitations by leveraging sensor data and machine learning techniques to anticipate equipment failures before they occur. This study presents the development and evaluation of machine learning-based predictive maintenance models for industrial manufacturing equipment using multi-sensor operational data. Vibration, temperature, acoustic, and load parameters were analyzed to identify degradation patterns and failure signatures. Feature extraction and selection techniques were applied to enhance model robustness, followed by training of supervised learning algorithms including random forest, support vector machines, and gradient boosting classifiers. Model performance was evaluated using accuracy, precision, recall, and remaining useful life estimation capability. Results demonstrate that data-driven predictive maintenance significantly reduces unplanned downtime and improves maintenance scheduling efficiency, confirming its potential as a key enabler of smart manufacturing systems.