Advanced Modelling for Diagnosis of Rotating Electrical Machines
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F3: Power Electronics".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 46
Special Issue Editors
Interests: induction motor diagnostics; numerical modeling of electrical machines; advanced automation processes and electrical installations
Interests: condition monitoring of electrical machines; applications of signal analysis techniques to electrical engineering and efficiency in electric power applications
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis of electrical machines; control of electrical machines and drives
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Monitoring and identifying faults in electrical machinery is both a technical and financial concern in industry. The diagnosis of faults in rotating electrical machines constitutes a vital aspect of a comprehensive monitoring system designed to enhance reliability and serviceability. Moreover, techniques for early fault detection would allow for the implementation of predictive maintenance systems, which are essential in modern industry to prevent incipient failures leading to catastrophic ones. In this context, numerical models that allow for reproducing in detail the operation of machines under failure conditions and analytical models capable of working in real time are essential tools for the development of new advanced diagnostic methods.
This Special Issue is focused to new contributions in the field of modeling electrical machines under faulty conditions. Papers on numerical methods’ optimization for reproducing phenomena linked with different kind of faults, techniques for reducing computational burden, and analytical models that can be useful as digital twins or easily implementable in HIL devices, or embedded into pre-existing devices, are welcome.
Topics of interest include, but are not limited to, the following:
- Current/field/mechanic-based diagnosis of electrical machines;
- Diagnosis in steady/transient state;
- Advances in the modeling of electrical machines;
- Model reduction techniques;
- Prognosis and estimation modeling for remaining useful life;
- Digital twins and digital-twin-enabling technologies for the modeling and simulation of faulty electrical machines;
- Artificial intelligence (AI) and machine learning (ML) methods for the predictive maintenance and diagnosis of faulty electrical machines.
Dr. Carla Terron-Santiago
Prof. Dr. Martin Riera-Guasp
Prof. Dr. Javier Martinez-Roman
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. Energies 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 2600 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
- electric machines
- condition monitoring
- fault diagnosis
- prognostics
- model-based fault diagnosis
- numerical modeling
- analytical modeling
- digital twins
