AI-Driven Predictive Maintenance for Smart Manufacturing Systems

Pardeep,

doi:https://doi.org/10.55041/ijcope.v2i3.157

Volume 02, Issue 03

Published on: March 2026 2026

AI-DRIVEN PREDICTIVE MAINTENANCE FOR SMART MANUFACTURING SYSTEMS

Pardeep

Deepak Anand

MERI College of Engineering and Technology Bahadurgarh.

DOI:https://doi.org/10.55041/ijcope.v2i3.157

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

The increasing complexity of modern manufacturing systems has necessitated the adoption of intelligent maintenance strategies to ensure reliability, efficiency, and cost-effectiveness. Traditional maintenance approaches, such as reactive and preventive maintenance, often lead to unexpected failures or unnecessary downtime. In this context, predictive maintenance (PdM) powered by artificial intelligence (AI) has emerged as a transformative solution for smart manufacturing environments. This study presents an AI-driven predictive maintenance framework designed for industrial systems using real-time sensor data and machine learning techniques. The proposed approach integrates data acquisition, feature extraction, and predictive modeling to identify potential failures before they occur. A simulated experimental dataset representing machine operating conditions was used to evaluate system performance.

How to Cite this Paper

Pardeep, (2026). AI-Driven Predictive Maintenance for Smart Manufacturing Systems. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(03). https://doi.org/10.55041/ijcope.v2i3.157

Pardeep, . "AI-Driven Predictive Maintenance for Smart Manufacturing Systems." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i3.157.

Pardeep, . "AI-Driven Predictive Maintenance for Smart Manufacturing Systems." International Journal of Creative and Open Research in Engineering and Management 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i3.157.

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•Peer Review Type: Double-Blind Peer Review
•Published on: Mar 27 2026

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