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Big-Data-Driven Advances in Smart Maintenance and Industry 4.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1916

Special Issue Editors


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Guest Editor
School of Engineering, University of Seville, 41092 Seville, Spain
Interests: smart maintenance; Industry 4.0; big data driven
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this Special Issue, we explore the revolutionary potential of digitalisation and its importance to the advancement of smart maintenance, factories and Industry 4.0. The papers explore key themes of maintenance, sustainability, and digitalisation. This Special Issue not only highlights the current state of research and practice but also sets the stage for future innovations in engineering maintenance and the sustainable digital transformation of industries.

The Special Issue mainly focuses on the following aspects:

  • The role of digitalisation in maintaining critical engineering assets;
  • Insights into risk management and digital strategies for engineering assets;
  • Discussions on digital transformation of infrastructure management;
  • The exploration of digital twins for integral life cycle management in critical infrastructures.
  • Emphasis on the importance of data management and its role in smart maintenance transformation.

This Special Issue aims to explore current trends and future directions in engineering maintenance, focusing on the integration of digital technologies to drive innovation and sustainability in smart maintenance and industry 4.0.

Prof. Dr. Vicente González-Prida
Prof. Dr. Adolfo Márquez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • critical infrastructure
  • digital twins
  • digitalisation
  • facility management
  • Industry 4.0
  • information and data assets
  • life cycle management
  • maintenance
  • performance and condition of physical assets
  • risk and reliability analysis
  • servitization
  • smart factory
  • standards
  • sustainability

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Published Papers (1 paper)

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Research

24 pages, 2974 KiB  
Article
Digitalization and Dynamic Criticality Analysis for Railway Asset Management
by Mauricio Rodríguez Hernández, Antonio Sánchez-Herguedas, Vicente González-Prida, Sebastián Soto Contreras and Adolfo Crespo Márquez
Appl. Sci. 2024, 14(22), 10642; https://doi.org/10.3390/app142210642 - 18 Nov 2024
Viewed by 1065
Abstract
The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. [...] Read more.
The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment. Full article
(This article belongs to the Special Issue Big-Data-Driven Advances in Smart Maintenance and Industry 4.0)
Show Figures

Figure 1

Figure 1
<p>Framework for the Criticality Analysis in Railway.</p>
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<p>Visual representation of the data flows and business rules.</p>
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<p>Example of a rules for a severity factor (Safety).</p>
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<p>Example of the resulting criticality matrix.</p>
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