AI-Driven Predictive Maintenance Framework for Smart Manufacturing: Real-Time Deployment, Multi-Sensor Fusion and Scalable Efficiency Optimization

Purushotham Endla, Sunil Bhardwaj, P. Mathiyalagan, K. Akila, P. Sanjeevkumar, M. Srinivasulu

2025

Abstract

In the changing world of Industry 4.0, predictive maintenance with artificial intelligence (AI) is a massive shift from the status quo of how mass production sites plan all of their maintenance methodologies. In this paper, we propose a novel AI-based predictive maintenance framework for smart manufacturing systems focusing on real-time deployment, sensor variety and cross-domain scalability. By systematically addressing the challenges faced by previous works such as over dependence on synthetic data, over focus on a specific domain, no real-time validation and low model explainability, our work presents a holistic approach that integrates multi-sensor data fusion, energy-efficient edge computing and explainable AI. The framework is both accurate, flexible and easy to interpret by the user, as demonstrated with actual industrial samples. It is also back-ward compatible with existing systems, which is highly attractive for deploying in modern as well as existing manufacturing plants. This not only improves technical performance, but enables maintenance teams with actionable information that can decrease downtime and maintenance costs.

Paper Citation

in Harvard Style

Endla P., Bhardwaj S., Mathiyalagan P., Akila K., Sanjeevkumar P. and Srinivasulu M. (2025). AI-Driven Predictive Maintenance Framework for Smart Manufacturing: Real-Time Deployment, Multi-Sensor Fusion and Scalable Efficiency Optimization. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 782-789. DOI: 10.5220/0013943600004919

in Bibtex Style

@conference{icrdicct`2525,
author={Purushotham Endla and Sunil Bhardwaj and P. Mathiyalagan and K. Akila and P. Sanjeevkumar and M. Srinivasulu},
title={AI-Driven Predictive Maintenance Framework for Smart Manufacturing: Real-Time Deployment, Multi-Sensor Fusion and Scalable Efficiency Optimization},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={782-789},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013943600004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - AI-Driven Predictive Maintenance Framework for Smart Manufacturing: Real-Time Deployment, Multi-Sensor Fusion and Scalable Efficiency Optimization
SN - 978-989-758-777-1
AU - Endla P.
AU - Bhardwaj S.
AU - Mathiyalagan P.
AU - Akila K.
AU - Sanjeevkumar P.
AU - Srinivasulu M.
PY - 2025
SP - 782
EP - 789
DO - 10.5220/0013943600004919
PB - SciTePress