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Digital Twins and Advanced Fault Modeling in the Condition Monitoring...
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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: 30 August 2026 | Viewed by 1785

Special Issue Editor

Dr. Nur Sarma

E-Mail Website
Guest Editor
Department of Engineering, Durham University, Durham DH1 3LE, UK
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 250 words) can be sent to the Editorial Office for assessment.

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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Published Papers (2 papers)

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Research

25 pages, 6049 KB  
Article
FMEA-Guided Selective Multi-Fidelity Modeling for Computationally Efficient Digital Twin-Based Fault Detection
by Euicheol Shin, Seohee Jang, Seongwan Kim, Chan Roh, Heemoon Kim, Jongsu Kim, Daehong Lee and Hyeonmin Jeon
Machines 2026, 14(5), 480; https://doi.org/10.3390/machines14050480 - 24 Apr 2026
Viewed by 364
Abstract
Autonomous navigation technologies have been widely adopted in the automotive and aviation sectors, significantly reducing human-error-induced accidents and operational costs. However, their application to maritime systems remains limited due to the complexity of conventional propulsion systems. Electric propulsion ships, with well-defined system boundaries [...] Read more.
Autonomous navigation technologies have been widely adopted in the automotive and aviation sectors, significantly reducing human-error-induced accidents and operational costs. However, their application to maritime systems remains limited due to the complexity of conventional propulsion systems. Electric propulsion ships, with well-defined system boundaries and accessible operational data, offer a promising platform for autonomous navigation. In this study, we propose an FMEA-guided selective multi-fidelity digital twin framework for fault detection, where model fidelity is adaptively selected between low- and high-fidelity models based on risk priority numbers derived from failure mode and effects analysis. This approach enables selective execution of computationally expensive models only under high-risk conditions, thereby improving computational efficiency. In addition, a sliding window-based algebraic aggregation method is employed to achieve lightweight and real-time fault diagnosis. The proposed framework is validated using operational sensor data from a 100 kW electric propulsion ship under multiple fault scenarios, including power supply faults and signal anomalies. Experimental results show that the proposed method reduces computational cost while maintaining stable real-time performance, compared to conventional data-driven AI-based approaches. These results demonstrate that the proposed framework provides an effective and efficient solution for enhancing the reliability and safety of autonomous ship systems. Full article
(This article belongs to the Special Issue Digital Twins and Advanced Fault Modeling in the Condition Monitoring of Electric Machines)
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19 pages, 8369 KB  
Article
An Ensemble-LSTM-Based Framework for Improved Prognostics and Health Management of Milling Machine Cutting Tools
by Sahbi Wannes, Lotfi Chaouech, Jaouher Ben Ali, Eric Bechhoefer and Mohamed Benbouzid
Machines 2026, 14(1), 12; https://doi.org/10.3390/machines14010012 - 20 Dec 2025
Viewed by 860
Abstract
Accurate Prognostics and Health Management (PHM) of cutting tools in Computer Numerical Control (CNC) milling machines is essential for minimizing downtime, improving product quality, and reducing maintenance costs. Previous studies have frequently applied deep learning, particularly Long Short-Term Memory (LSTM) neural networks, for [...] Read more.
Accurate Prognostics and Health Management (PHM) of cutting tools in Computer Numerical Control (CNC) milling machines is essential for minimizing downtime, improving product quality, and reducing maintenance costs. Previous studies have frequently applied deep learning, particularly Long Short-Term Memory (LSTM) neural networks, for tool wear prediction and Remaining Useful Life (RUL) prediction. However, they often rely on simplified datasets or single architectures limiting industrial relevance. This study proposes a novel ensemble-LSTM framework that combines LSTM, BiLSTM, Stacked LSTM, and Stacked BiLSTM architectures using a GRU-based meta-learner to exploit their complementary strengths. The framework is evaluated using the publicly available PHM’2010 milling dataset, a well-established industrial benchmark comprising comprehensive time-series sensor measurements collected under variable loads and realistic machining conditions. Experimental results show that the ensemble-LSTM outperforms individual LSTM models, achieving an RMSE of 2.4018 and an MAE of 1.9969, accurately capturing progressive tool wear trends and adapting to unseen operating conditions. The approach provides a robust, reliable solution for real-time predictive maintenance and demonstrates strong potential for industrial tool condition monitoring. Full article
(This article belongs to the Special Issue Digital Twins and Advanced Fault Modeling in the Condition Monitoring of Electric Machines)
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