Improving Predictive Maintenance in Industrial Environments via IIoT and Machine Learning

Abstract

Optimizing maintenance procedures is essential in today’s industrial settings to reduce downtime and increase operational effectiveness. To improve predictive maintenance in industrial settings, this article investigates the combination of machine learning (ML) techniques and the Industrial Internet of Things (IIoT). The goal of this research is to advance predictive maintenance in industrial settings by integrating ML with IIoT in a seamless manner. Addressing the complexities of industrial systems and limitations of traditional maintenance methods, this study presents a methodology leveraging four distinct ML models. The technique includes a thorough assessment of these models’ correctness, revealing differences that highlight the significance of a careful model selection procedure. The current investigation analysis finds the most effective model for predictive maintenance activities using thorough data analysis and visualization. Our work offers a potential path forward for the industrial sector and provides insights into the complex interactions between IIoT and ML. This study lays the groundwork for future developments in predictive maintenance, which will reduce downtime and extend the life of industrial equipment.