Artificial Intelligence Applications for Diagnosis, Maintenance and Control of Electric Machines
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 39
Special Issue Editors
Interests: intelligent fault diagnosis; condition monitoring; predictive maintenance; rolling bearings; generative AI; machine learning; deep learning; transfer learning; rotating machinery; generative adversarial networks; explainable AI; machine design; artificial intelligence; image processing
Special Issues, Collections and Topics in MDPI journals
Interests: AC machines; high-frequency modelling; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Interests: electrical machines; fault diagnosis; reliability; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Nowadays, the reliability of electrical machines is critical for many industrial sectors. In recent years, the integration of Artificial Intelligence (AI) techniques has opened new perspectives for condition monitoring, diagnosis, maintenance, and control to increase operational efficiency, reduce costs, and prevent catastrophic failures.
Modern AI frameworks are increasingly designed to handle multimodal data by leveraging large-scale models; recent advances in language modeling have enabled the integration of unstructured data alongside traditional sensor information. These developments are opening new opportunities for the design of diagnostic, maintenance, and control approaches that can be generalized across different types of electrical machines.
This Special Issue welcomes original contributions, both theoretical and applied, that demonstrate the impact of AI techniques on the diagnosis, maintenance, and control of electrical machines. Papers are particularly encouraged in the following areas:
The application of AI and ML for fault detection in electric motors, generators, and drives; use of deep learning and neural networks for condition monitoring on different types of signals (vibrations, currents, acoustic emissions, magnetic fields); Remaining Useful Life (RUL) prediction and prognostics techniques; development of solutions based on edge AI, sensor fusion, and real-time applications; application of large-scale and multipurpose AI models for the diagnosis, monitoring, and control of electric machines; integration of unstructured data sources into AI-based diagnostic and control systems; industrial case studies in the electric mobility, energy, railway, aerospace, and manufacturing sectors. Review articles offering a comprehensive overview of recent developments in the field are also welcome.
Dr. Luigi Gianpio Di Maggio
Dr. Jose E. Ruiz-Sarrio
Prof. Dr. Jose Alfonso Antonino-Daviu
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
- intelligent fault diagnosis
- condition monitoring
- RUL estimation
- machine fault diagnosis
- artificial intelligence
- electrical machines
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