Digital Twins and Advanced Fault Modeling in the Condition Monitoring 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: 28 February 2026 | Viewed by 43
Special Issue Editor
Interests: condition monitoring; fault analysis and fault mitigation strategies for renewable energy systems; modelling and analysis of electrical systems and machines; electrical power systems modelling, analysis and design; drive and electric generator systems; development and implementation of advanced control strategies for electrical drive and power systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Electric machines play a pivotal role in creating a sustainable future by performing critical tasks in electric mobility and renewable energy generation. In these roles, their reliable and continuous operation is increasingly important to ensure system efficiency, safety, and cost-effectiveness. Therefore, the implementation of enhanced condition monitoring and fault detection techniques for electric machines is vital for preventing unexpected failures, minimizing downtime, and extending the lifespan of both the machines and the overall system.
Digital twins and advanced fault modeling have appeared as powerful tools in advanced digital technologies for enhancing the studies and research on electric machine reliability, predictive maintenance, and efficient operation compared to the traditionally established methods. A digital twin serves as a dynamic, real-time, virtual replica of an electric machine, which enables detailed analysis of electric machine operation under varying operating conditions. This feature of digital twins, as a result, leads to a more accurate investigation and better understanding of the monitoring and fault detection of electric machines. Additionally, advanced fault modeling allows researchers to replicate a wide range of fault scenarios, including electrical, mechanical, and thermal, and to improve the understanding of the most significant operational system parameters, and use this knowledge to enhance the accuracy, reliability, and efficiency of electric machines.
This is a call for papers for a Special Issue on “Digital Twins and Advanced Fault Modeling in the Condition Monitoring of Electric Machines”. This Special Issue aims to provide a platform for scientists and researchers to present their latest advancements, showcase significant achievements, and discuss ongoing challenges and future directions in this rapidly evolving field.
Submitted manuscripts are expected to offer original ideas and meaningful contributions to both theoretical understanding and practical applications.
Topics of interest include, but are not limited to, the following:
- Development and application of digital twin technologies for electric machine monitoring;
- Advanced fault modeling techniques covering electrical, mechanical, and thermal faults;
- Integration of digital twins with machine learning and AI for predictive maintenance;
- Simulation and validation of fault scenarios using enhanced modeling approaches;
- Data-driven diagnostics and prognostics for electric machines;
- Case studies demonstrating practical deployment and benefits of these technologies.
Dr. Nur Sarma
Guest Editor
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
- digital twin
- electric machine monitoring
- fault modeling and simulation
- predictive maintenance
- condition monitoring
- fault detection
- fault diagnosis
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