Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)

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A Real-time Predictive Maintenance System using Machine Learning and IoT for Industrial Equipment Monitoring

Authors
Debabrata Bej1, Arnab De2, *, Abhishek Raj3, Ankan Bhattacharya4, Bappadittya Roy5, Kailash Pati Dutta6
1Department of ECE, NIT, Durgapur, West Bengal, India
2Department of ACSE, Vignan’s Foundation for Science, Technology & Research, Guntur, AP, India
3TATA Elxsi, Bengaluru, India
4Dept. of ECE, Hooghly Engineering &Technology College, Hooghly, India
5School of Electronics Engg., VIT-AP University, Amaravati, AP, India
6Dept of Computer Science Engineering and Information Technology, Jharkhand Rai University, Ranchi, India
*Corresponding author. Email: ade.ece1990@gmail.com
Corresponding Author
Arnab De
Available Online 17 July 2025.
DOI
10.2991/978-94-6463-787-8_18How to use a DOI?
Keywords
Predictive Maintenance; IoT Sensors; Machine Learning; Fault Detection; Operational Efficiency
Abstract

Industrial equipment failures lead to costly downtime and maintenance inefficiencies. This research presents a real-time predictive maintenance system leveraging IoT sensors and machine learning (ML) models for automated fault detection and performance optimization. The system integrates IoT-based sensors, real-time data analytics, and ML algorithms to monitor industrial equipment, predict failures, and optimize maintenance processes. Sensors collect real-time operational data from industry, which is securely transmitted using efficient protocols and stored for further analysis. Advanced ML algorithms analyze this data to detect patterns indicative of equipment failures, providing predictive insights to enhance reliability. A web-based interface enables employees to monitor equipment status, receive maintenance recommendations, and request actions, ensuring precision in data handling while reducing manual errors. The study highlights the system’s potential to improve accuracy, reduce maintenance costs, and increase operational efficiency by minimizing downtime and enhancing reliability.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
Series
Advances in Intelligent Systems Research
Publication Date
17 July 2025
ISBN
978-94-6463-787-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-787-8_18How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Debabrata Bej
AU  - Arnab De
AU  - Abhishek Raj
AU  - Ankan Bhattacharya
AU  - Bappadittya Roy
AU  - Kailash Pati Dutta
PY  - 2025
DA  - 2025/07/17
TI  - A Real-time Predictive Maintenance System using Machine Learning and IoT for Industrial Equipment Monitoring
BT  - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025)
PB  - Atlantis Press
SP  - 201
EP  - 213
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-787-8_18
DO  - 10.2991/978-94-6463-787-8_18
ID  - Bej2025
ER  -