Abstract
In the era of Industry 4.0, the integration of Machine Learning (ML) with the Industrial Internet of Things (IIoT) has transformed predictive maintenance into a powerful tool for enhancing operational efficiency and minimizing unplanned downtime. This study provides a comprehensive comparative analysis of various machine learning algorithms applied in predictive maintenance within IIoT environments. We evaluate the performance of algorithms such as Random Forest, Support Vector Machines, Neural Networks, and Gradient Boosting in terms of accuracy, computational efficiency, and scalability. Our research explores the nuances of these algorithms when applied to different industrial datasets, highlighting their strengths and limitations in realworld scenarios. Furthermore, we discuss the practical applications of predictive maintenance in diverse industrial sectors, emphasizing case studies where specific ML techniques have led to significant cost savings and operational improvements. This study not only serves as a guide for selecting appropriate ML algorithms for predictive maintenance but also contributes to the ongoing discourse on optimizing IIoT systems for maximum reliability and efficiency. The findings underscore the importance of algorithm selection tailored to specific industrial needs and offer actionable insights for practitioners and researchers in the field.
Published Version
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