Special Issue "Modelling for Reliability and Maintenance Engineering"

Special Issue Editors

Assoc. Prof. Phuc Do
Guest Editor
University of Lorraine, Campus sciences BP70239, 54506 Vandoeuvre Cedex, France
Interests: degradation and reliability modelling; prognostics of key performance indicators; maintenance modelling and optimization
Assoc. Prof. Cristiano Cavalcante
Guest Editor
Universidade Federal de Pernambuco (Federal University of Pernambuco), Av. da Arquitetura, s/n. Prédio do Departamento de Engenharia de Produção, Sala 201 A. CEP: 50740-550, Cidade Universitária, Recife-PE, Brasil
Interests: operational research; maintenance modelling and optimization; risk; reliability; safety; warehouse management and logistics

Special Issue Information

Dear Colleagues,

The growing human dependence on technological systems, as well as the ground-breaking changes in production processes, has recently placed maintenance and reliability factors at a hitherto unforeseen level of importance. New techniques have been emerging from different areas and are being associated with classical approaches in maintenance and reliability to help to handle massive amounts of data, the need to do this being one of the main features of the current manufacture revolution. Thus, a broader effort in research is imperative so that the role of reliability and maintenance in these new challenges can be adequately defined and able to meet the constant demands for this. For the reasons mentioned above, this Special Issue of Modelling seeks to attract relevant contributions on reliability and maintenance modeling. Therefore, the Guest Editors invite submissions with an original perspective and advanced thinking on this topic and related issues. We welcome original research papers with theoretical development and solid empirical grounding on any one or more of (but not limited to) the following topics:

  • degradation modeling and reliability assessment;
  • reliability analysis;
  • prognostics and health management;
  • reliability and maintenance engineering;
  • digital twin for reliability and maintenance;
  • reinforcement learning for maintenance decision-making;
  • machine learning for reliability;
  • reliability and maintenance modeling for cyber–physical systems;
  • maintenance for industry 4.0;
  • life cycle/performance analysis;
  • spare parts supply chain management;
  • warranty management and data analysis.

Assoc. Prof. Phuc Do
Assoc. Prof. Cristiano Cavalcante
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 papers will be 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. Modelling—International Open Access Journal of Modelling in Engineering Science is an international peer-reviewed open access quarterly 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 1000 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.


  • degradation modeling
  • reliability
  • maintenance
  • prognostics
  • digital twin
  • machine learning
  • cyber–physical systems

Published Papers

This special issue is now open for submission.
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