Special Issue "Fault Diagnosis Methods Based on Information Theory or Machine Learning: From Theory to Application"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 30 June 2022.

Special Issue Editors

Dr. Claude Delpha
E-Mail Website
Guest Editor
CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, Université Paris Saclay, 91400 Orsay, France
Interests: data and signal processing; incipient fault diagnosis; detection and estimation; data hiding; watermarking; complex systems; statistical learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Demba Diallo
E-Mail Website
Guest Editor
CNRS, CentraleSupélec, Group of Electrical Engineering of Paris, Université Paris Saclay, 91400 Orsay, France
Interests: electrical drives; incipient fault diagnosis; fault-tolerant control; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, system health monitoring is a main concern in industrial and academic research due to the increasing requirements imposed by safety rules and the demand for the reduction of maintenance costs. Typical applications include the monitoring of transportation systems (automobiles, aircrafts, and trains); energy generation, transportation, storage, and distribution systems (nuclear power plants, wind turbines, smart grids, etc.); and industrial processes.

In smart systems, faults are detected at an early stage and classified, and the system lifetime is predicted to optimize maintenance operations. In order to meet these requirements, new monitoring algorithms are continuously developed. These algorithms integrate state-of-the-art signal and data analysis/processing techniques, entropy-based study, statistical learning, and machine learning or deep learning approaches.

This Issue will focus on the application of all of these signal and analysis/processing techniques for the health monitoring of complex systems. Particular attention is paid either to statistical/entropy-based detection/estimation techniques and machine learning/deep learning-based diagnosis techniques. Many approaches are concerned with topics such as quantitative approaches with wide and efficient physical modeling, qualitative approaches, and data driven approaches. For this Issue, either theoretical or applicative works will be considered. Particular attention will be paid to applications in tune with time such as human health, renewable-energy-based systems, energy conversion systems, smart grids, mechanical systems, vehicular and industrial applications, etc.

Dr. Claude Delpha
Prof. Dr. Demba Diallo
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. Entropy 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 1800 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

  • fault detection and diagnosis
  • data and signal processing for diagnosis
  • statistical analysis and learning
  • fault and system modeling
  • detection methodologies for diagnosis
  • distance measures for diagnosis
  • estimation methodologies in fault diagnosis
  • fault isolation and classification
  • machine learning for fault diagnosis
  • deep learning for fault diagnosis
  • fault prognosis and RUL
  • application to engineering systems

Published Papers

This special issue is now open for submission.
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