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The Role of Artificial Intelligence in Predictive Maintenance for Industrial Engineering Systems
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Industrial Engineering
Abstract
Predictive maintenance has emerged as a transformative strategy in industrial engineering, enabling early detection of equipment failures and minimizing unplanned downtime. The integration of Artificial Intelligence (AI) into predictive maintenance systems enhances their efficiency by leveraging machine learning, deep learning, and real-time data analytics. This paper explores the role of AI in predictive maintenance within industrial systems, examining intelligent models that process sensor data, identify failure patterns, and estimate Remaining Useful Life (RUL). The study reviews current AI methodologies, industrial case applications, benefits, challenges, and future research directions. Findings suggest that AI-driven predictive maintenance improves reliability, optimizes maintenance scheduling, and reduces costs, contributing to smarter and more sustainable industrial operations.
Keywords
Artificial Intelligence, Predictive Maintenance, Industrial Engineering, Machine Learning, Industrial IoT
How to cite this paper
@article{1709941,
author = {Mohammed Abdus Salam},
title = {The Role of Artificial Intelligence in Predictive Maintenance for Industrial Engineering Systems},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {6},
pages = {1121-1124},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709941.pdf},
abstract = {Predictive maintenance has emerged as a transformative strategy in industrial engineering, enabling early detection of equipment failures and minimizing unplanned downtime. The integration of Artificial Intelligence (AI) into predictive maintenance systems enhances their efficiency by leveraging machine learning, deep learning, and real-time data analytics. This paper explores the role of AI in predictive maintenance within industrial systems, examining intelligent models that process sensor data, identify failure patterns, and estimate Remaining Useful Life (RUL). The study reviews current AI methodologies, industrial case applications, benefits, challenges, and future research directions. Findings suggest that AI-driven predictive maintenance improves reliability, optimizes maintenance scheduling, and reduces costs, contributing to smarter and more sustainable industrial operations.},
keywords = {Artificial Intelligence, Predictive Maintenance, Industrial Engineering, Machine Learning, Industrial IoT},
month = {December},
}