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research-article

Catalyzing industrial evolution: : A dynamic maintenance framework for maintenance 4.0 optimization

Authors:

Mario Di Nardo,

Assunta Cammardella,

Mengchu SongAuthors Info & Claims

Volume 196, Issue C

https://doi.org/10.1016/j.cie.2024.110469

Published: 18 November 2024 Publication History

Highlights

•

Dynamic grouping method that improves maintenance for multi-component systems.

•

Opportunistic maintenance optimizes costs and reduces downtime intelligently.

•

IoT, AI, and big data analytics drive smarter, predictive Maintenance 4.0 strategies.

Abstract

The concept of Maintenance 4.0 represents a transformative shift in industrial maintenance practices, leveraging cutting-edge technologies within the broader framework of Industry 4.0. This paradigm aims to revolutionize the planning, execution, and monitoring of maintenance tasks by integrating artificial intelligence, machine learning, big data analytics, the Internet of Things (IoT), and predictive analytics. The resulting holistic approach enhances equipment longevity, minimizes downtime, optimizes maintenance schedules, and improves overall process efficiency and cost-effectiveness for organizations.

This study focuses on developing a novel dynamic maintenance framework that expands upon the traditional moving horizon approach. The primary objective is to categorize individual proactive maintenance (PM) activities within the planning horizon into PM groups, thereby establishing a fixed system maintenance schedule. This innovation not only improves practical implementation in the industry but also contributes to theoretical advancements.

The distinctive advantage of this methodology lies in its capacity to dynamically revise upkeep data, significantly reducing the likelihood of operational failure. By bridging the gap between theory and application, this research contributes to the ongoing evolution of Maintenance 4.0, offering a more proactive, intelligent, and data-driven approach to industrial maintenance.

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Information & Contributors

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cover image Computers and Industrial Engineering

Computers and Industrial Engineering Volume 196, Issue C

Oct 2024

1073 pages

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 18 November 2024

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