Machine Learning Algorithms for Predictive Maintenance in Industrial Systems

Abstract

In the rapidly evolving landscape of industrial systems, the integration of machine learning (ML) algorithms for predictive maintenance has emerged as a pivotal strategy for enhancing operational efficiency and minimizing downtime. This paper explores various ML techniques, including supervised and unsupervised learning methods, to analyze and predict equipment failures based on historical data and real-time sensor inputs. By employing algorithms such as decision trees, random forests, and neural networks, the study demonstrates significant improvements in maintenance scheduling, leading to reduced costs and increased reliability. The research also highlights the importance of feature selection and data preprocessing in optimizing model performance. Case studies from diverse industrial sectors are presented to illustrate the practical applications and benefits of these ML approaches in predictive maintenance. Ultimately, this paper aims to provide a comprehensive overview of how machine learning can transform maintenance strategies, fostering a proactive rather than reactive approach to industrial system management.