Predictive Maintenance in IoT Systems Using Ensemble Learning Approaches

Abstract

Predictive maintenance (PdM) has become a critical application of the Internet of Things (IoT), leveraging data-driven insights to prevent system failures and optimize operational efficiency. By using IoT sensors, large volumes of data are collected, and machine learning (ML) techniques are applied to predict when equipment or machinery will likely fail. Ensemble learning, a machine learning paradigm that combines multiple models to improve predictive performance, plays a significant role in predictive maintenance within IoT systems. This paper explores the use of ensemble learning approaches, such as Random Forest (RF), Gradient Boosting Machine (GBM), and AdaBoost, in enhancing the accuracy and reliability of maintenance predictions in industrial IoT settings. We discuss various ensemble learning models and their suitability for real-time fault prediction, along with challenges such as data imbalance, noise, and scalability issues in large-scale IoT environments. Additionally, the paper reviews state-of-the-art PdM techniques, evaluates their integration with IoT, and investigates performance metrics, including precision, recall, and F1-score. The findings indicate that ensemble models, by aggregating multiple weak learners, offer significant improvements over single-model approaches, particularly in complex and dynamic IoT systems. Finally, the paper proposes a framework for implementing ensemble-based predictive maintenance in IoT systems, outlining key methodologies, challenges, and future research directions to further refine PdM applications in industry 4.0.

DOI - https://doi.org/10.65525/SVUP.9788199565401.2026.196-202