Revolutionizing Conveyor Belt Systems: Empowering Predictive Maintenance with IoT, Cloud, and Machine Learning

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 6

Year of Publication : 2024

Authors : P.V.S Anusha, P. Swapna, D.V. Rama Koti Reddy

10.14445/23488379/IJEEE-V11I6P124

How to Cite?

P.V.S Anusha, P. Swapna, D.V. Rama Koti Reddy, "Revolutionizing Conveyor Belt Systems: Empowering Predictive Maintenance with IoT, Cloud, and Machine Learning," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 6, pp. 224-233, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I6P124

Abstract:

 The ever-increasing volume of data necessitates an effective monitoring system to support decision-making processes. In the Industry 4.0 landscape, Artificial Intelligence (AI) is reshaping manufacturing by leveraging Internet of Things (IoT) technologies and machine learning methods.  In this paper, a data-driven predictive maintenance system for conveyor belt systems in manufacturing using IoT technologies and machine learning methods is proposed. The system uses real-time data from IoT devices such as accelerometers, temperature, and current sensors deployed on conveyor belts integrated with ESP32 and AWS cloud infrastructure. The study evaluates the efficacy of the developed predictive maintenance system using real-world IoT data from manufacturing environments and machine learning.  The top-performing algorithm is the extra trees classifier with the highest accuracy, which shows superior performance across multiple metrics. The results demonstrate the system's success in identifying potential failure indicators, thereby mitigating production downtimes. The paper highlights the significance of the belt conveyor system in various industries and the need for efficient maintenance methods to ensure smooth operation.

Keywords:

Internet of Things (IoT), Machine Learning, Conveyor belt, Predictive maintenance, Extra trees.

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