MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE IN MANUFACTURING

Abstract

Predictive maintenance (PdM) powered by machine learning (ML) has transformed manufacturing by enabling timely detection of equipment failures, thus reducing downtime and operational costs. This article examines various ML algorithms used for PdM, including decision trees, support vector machines, neural networks, and ensemble techniques. It discusses challenges in implementing ML-based PdM in manufacturing settings, performance evaluation of different algorithms, and prospects for integrating ML in Pakistan’s manufacturing industry. The study emphasizes the potential benefits of ML-driven PdM in enhancing manufacturing efficiency aligned with Industry 4.0 standards.