Machine Learning for Machinery Prognostics and Health Management

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 324

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, Université de Mons, 31, 7000 Mons, Belgium
Interests: estimation of residual lifetime of industrial equipment from physics-of-failure models; statistical analysis; degradation measurements or data-driven machine learning from the perspective of an optimal predictive maintenance policy

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Guest Editor
École Nationale des Sciences Appliquées d'Oujda, Oujda, Morocco
Interests: maintenance 4.0; energy efficiency in industry; quality, safety and environment; numerical; digital transitions

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Guest Editor
Laboratoire Génie Industriel, CentraleSupelec, Ecole Centrale Casablanca, Bouskoura, Ville Verte- 27182, Morocco
Interests: maintenance management; total productive maintenance; lean six sigma; data analytics for operations management

Special Issue Information

Dear Colleagues,

Condition monitoring and prognostics should contribute to sustainable asset management. Knowledge, information and data must be used to detect anomalies, diagnose the causes of failure, predict the health of the system and estimate the remaining useful life to decide on the appropriate maintenance action. Machine learning has offered new tools to analyze the data from the production process, quality control or maintenance data. Sustainability requirements have extended the scope of the objectives, providing environmental and social indicators to be optimized. Consequently, the data field is wider than ever and artificial intelligence makes it possible to achieve outstanding results that more traditional methods cannot achieve. This is still true for the scope of Machines, which includes systems and control engineering, machine diagnostics and prognostics (condition monitoring).

This Special Issue of Machines will focus on but not be limited to advances in the application of AI to the life-cycle management of electromechanical equipment. This special issue will provide an excellent opportunity to bring together researchers working on machine learning models and algorithms for machine condition monitoring and prognostics.

Dr. Pierre Dehombreux
Prof. Dr. Bachir Elkihel
Prof. Dr. Fouad Riane
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. Machines is an international peer-reviewed open access monthly 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

  • prognostics and health management (PHM)
  • condition monitoring and prognostics
  • machine learning
  • deep learning
  • fault diagnosis
  • remaining useful life
  • remaining life prediction
  • product lifecycle management
  • state-of-health (SOH) estimation

Published Papers

There is no accepted submissions to this special issue at this moment.
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