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