Advancements in AI for Predictive Maintenance in Industrial Systems
Author(s): S. Kuppaswami1 ,G. Swaroop Kumar2
Affiliation: 1,2Department of Computer Science Engineering Sri Ramakrishna Engineering College, Coimbatore, India.
Page No: 14-16-
Volume issue & Publishing Year: Volume 1 Issue 3,Aug-2024
Journal: International Journal of Modern Engineering and Management | IJMEM
ISSN NO: 3048-8230
DOI:
Abstract:
Predictive maintenance (PdM) represents a transformative approach in industrial systems management, utilizing advanced technologies to anticipate equipment failures and optimize maintenance schedules. Recent advancements in artificial intelligence (AI) have significantly enhanced the capabilities of PdM, leveraging machine learning, data analytics, and intelligent algorithms to improve prediction accuracy and operational efficiency. This paper explores the latest developments in AI-driven predictive maintenance technologies, including the integration of deep learning models, real-time data processing, and edge computing. We examine case studies and practical implementations across various industries, assess the impact of these advancements on maintenance practices, and identify emerging trends and future research directions. The findings indicate that AI-powered PdM systems offer substantial benefits in reducing downtime, extending equipment lifespan, and optimizing resource allocation.
Keywords:
Predictive Maintenance, Artificial Intelligence, Machine Learning, Deep Learning, Industrial Systems, Real-Time Data Processing
Reference:
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