Smart Design and Maintenance of Electrical 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 March 2026 | Viewed by 56
Special Issue Editors
Interests: AC machines; high-frequency modelling; fault diagnosis
Interests: electrical machines and drives; e-mobility; electromechanical systems
Interests: electrical machines; fault diagnosis; reliability; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Rotating electrical machines are widely used in key sectors such as industry, energy, and transportation, with a growing presence in modern mobility applications. Some of these applications pose challenges that traditional design and maintenance approaches fail to address. In the digital era, with the increasing demand for sustainability, new design and maintenance paradigms are emerging. In this context, the design of electrical machines, both in their traditional and more innovative forms, represents an interesting research topic. Particularly, research on data-based methods, innovative optimization approaches, PM-less designs, and novel machine topologies contributes to fulfilling challenging design and sustainability goals. Additionally, maintenance strategies such as fault diagnosis and the condition monitoring of electrical machines currently face similar challenges. Data-driven approaches leveraging machine learning algorithms continue to attract significant research interest. This trend, together with advanced signal processing tools and innovative sensing technologies, contributes to the development of novel monitoring and diagnosis methods.
The aim of this Special Issue is to present the latest advancements in emerging paradigms for the design and maintenance of electrical machines. Original research on more traditional approaches, as well as review articles, are also welcome. Possible research topics include, but are not limited to, the following:
- The design of electrical machines for emerging applications;
- Special electrical machines;
- The design optimization of electrical machines;
- Data-driven design;
- The simulation of abnormal operation in electrical machines;
- Smart sensors for electrical machine maintenance;
- Advanced signal processing tools;
- Data-driven fault diagnosis and condition monitoring;
- Vibration monitoring;
- Electrical signature analysis.
Dr. Jose E. Ruiz-Sarrio
Prof. Dr. Claudia Martis
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
- AC machines
- fault diagnosis
- design for sustainability
- machine learning
- condition monitoring
- optimization
- RUL prediction
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