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Machines
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Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives
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Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electrical Machines and Drives".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3137

Special Issue Editors

Dr. Davide Fonseca

E-Mail Website
Guest Editor
CISE — Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal
Interests: diagnosis and fault tolerance of electrical machines, power electronics and drives
Prof. Dr. Antonio J. Marques Cardoso

E-Mail Website
Guest Editor
CISE - Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal
Interests: diagnosis and fault tolerance of electrical machines, power electronics and drives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In response to the increasing efficiency requirements of electric drives and line-start electrical motors, rotor/secundary hybridization of synchronous motors has emerged as a viable solution that would increase both efficiency and fault tolerance. Due to hybridization, the motor torque is developed by more than one phenomenon. In this category, drives like a Line-Start Synchronous Motor drives develop torque either by induction in a squirrel cage or synchronously due to reluctance and/or permanent magnet flux, making them highly recommended for direct replacement of old induction motors. Drives like Permanent Magnet Assisted Synchronous Reluctance Motor drives develop their synchronous torque due to both the reluctance and permanent magnet flux being more fault tolerant in cases of rotor faults.

This Special Issue aims to explore the fault tolerance of synchronous motor drives through design, fault diagnosis and control. It targets increasing fault tolerance through new designs, evaluation of innovative designs of synchronous motors in terms of transient and steady-state faulty operations, as well as new fault tolerant control strategies. The Special Issue covers the improvement of analytical and finite-element models in performance prediction as well as the development of the experimental setup in measurement of the behavior of synchronous motor drives under different faults, such as stator faults, demagnetization, broken rotor bars, eccentricity, and power converter faults. It considers a wide range of innovative solutions such us hybrid rotor synchronous motors, including line-start permanent-magnet synchronous motors (LSPMSMs), line-start synchronous-reluctance motors (SynRMs), permanent magnet assisted synchronous reluctance motors (PMASynRMs), multi-phase Shincronous drives, etc.

This Special Issue focuses on:

  • Analytical and finite element modelling as well as optimization of more fault tolerant synchronous motor drives;
  • Transient and steady state operation prediction and measurement of faulty synchronous motor drives;
  • Design and analysis of special types of synchronous motor drives like line-start permanent-magnet assisted synchronous reluctance motors, multi-phase hybrid rotor synchronous motors, etc.;
  • Fault analysis of synchronous motor drives;
  • Thermal modelling synchronous motor drives, particularly under faulty conditions;
  • Condition monitoring of synchronous motor drives;
  • Application of artificial intelligence in the performance parameters estimation, analysis, and design of synchronous motor drives;
  • Solutions for variable speed capabilities as well as improved fault tolerant control solutions of synchronous motor drives;
  • Studies comparing the efficiency, power factor, and reliability of synchronous motor drives.

Dr. Davide Fonseca
Prof. Dr. Antonio J. Marques Cardoso
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

  • fault diagnostics
  • fault tolerance
  • synchronous motors
  • line-start synchronous motors
  • permanent magnet assisted synchronous reluctance motors

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Published Papers (4 papers)

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Research

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25 pages, 722 KiB  
Article
Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors
by Javier de las Morenas, Lidia M. Belmonte and Rafael Morales
Machines 2025, 13(5), 357; https://doi.org/10.3390/machines13050357 - 24 Apr 2025
Viewed by 101
Abstract
Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency and reliability. However, bearing faults remain a critical issue, necessitating robust fault detection strategies. This paper proposes an edge–fog–cloud architecture for bearing fault detection with a specific [...] Read more.
Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency and reliability. However, bearing faults remain a critical issue, necessitating robust fault detection strategies. This paper proposes an edge–fog–cloud architecture for bearing fault detection with a specific focus on implementing an efficient and non-intrusive edge-based solution. The methodology involves preprocessing motor current signals through fast Fourier transform (FFT) and Hilbert transform-based envelope analysis to extract harmonics without being masked by the fundamental supply frequency. These features are used to train machine learning models, considering variations in both speed and load. Experimental validation is conducted using the Paderborn University Bearing Dataset, demonstrating that the proposed approach achieves exceptional accuracy, precision, recall, and F1-score, exceeding 0.98 with models such as XGBoost, LightGBM, and CatBoost. While CatBoost exhibits the highest performance, LightGBM is selected as the optimal model due to its significantly reduced training time, making it well suited for edge computing applications. A comparison with prior studies confirms that the proposed method delivers competitive performance while utilizing fewer sensors, reducing hardware complexity. This research lays the groundwork for future predictive maintenance strategies ensuring real-time diagnostics and optimized industrial deployment. Full article
(This article belongs to the Special Issue Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives)
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12 pages, 7226 KiB  
Article
Deep Transfer Learning-Based Performance Prediction Considering 3-D Flux in Outer Rotor Interior Permanent Magnet Synchronous Motors
by Moo-Hyun Sung, Soo-Hwan Park, Kyoung-Soo Cha, Jae-Han Sim and Myung-Seop Lim
Machines 2025, 13(4), 302; https://doi.org/10.3390/machines13040302 - 7 Apr 2025
Viewed by 245
Abstract
Accurate performance prediction in the design phase of permanent magnet synchronous motors (PMSMs) is essential for optimizing efficiency and functionality. While 2-D finite element analysis (FEA) is commonly used due to its low computational cost, it overlooks important 3-D flux components such as [...] Read more.
Accurate performance prediction in the design phase of permanent magnet synchronous motors (PMSMs) is essential for optimizing efficiency and functionality. While 2-D finite element analysis (FEA) is commonly used due to its low computational cost, it overlooks important 3-D flux components such as axial leakage flux (ALF) and fringing flux (FF) that affect motor performance. Although 3-D FEA can account for these flux components, it is computationally expensive and impractical for rapid design iterations. To address this challenge, we propose a performance prediction method for interior permanent magnet synchronous motors (IPMSMs) that incorporates 3-D flux effects while reducing computational time. This method uses deep transfer learning (DTL) to transfer knowledge from a large 2-D FEA dataset to a smaller, computationally costly 3-D FEA dataset. The model is trained in 2-D FEA data and fine-tuned with 3-D FEA data to predict motor performance accurately, considering design variables such as stator diameter, axial length, and rotor design. The method is validated through 3-D FEA simulations and experimental testing, showing that it reduces computational time and accurately predicts motor characteristics compared to traditional 3-D FEA approaches. Full article
(This article belongs to the Special Issue Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives)
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25 pages, 9084 KiB  
Article
Real-Time Modeling of Static, Dynamic and Mixed Eccentricity in Permanent Magnet Synchronous Machines
by Ramón Pérez, Jérôme Cros and Mathieu Picard
Machines 2025, 13(2), 120; https://doi.org/10.3390/machines13020120 - 4 Feb 2025
Cited by 1 | Viewed by 745
Abstract
Eccentricity faults are one of the main causes that significantly affect the performance of permanent magnet synchronous machines (PMSMs). Monitoring eccentricity in real time could prevent failures by adapting operation conditions and maintenance schedule when early signs of deterioration are detected. This article [...] Read more.
Eccentricity faults are one of the main causes that significantly affect the performance of permanent magnet synchronous machines (PMSMs). Monitoring eccentricity in real time could prevent failures by adapting operation conditions and maintenance schedule when early signs of deterioration are detected. This article proposes making a circuit-type model of a permanent magnet machine with an easily configurable eccentricity for simulations and real-time analysis of signals under different operating conditions. The basis for the construction of the circuit model will be the simulation of the PMSM with 49 different coordinates of the rotor center, using the finite element analysis (FEA). The presence of eccentricity causes a variation in the inductances, the no-load flux and the expansion torque depending on the position of the rotor. The model proposes the use of bilinear interpolation (BI) to estimate the inductance matrix, the no-load flux vector captured by the stator winding and the cogging torque due to the presence of the magnets in the rotor, all of them for each rotor position. The validation is done by comparing the precision in the results of the machine’s self-inductances, the torque and the voltage waveform at the PMSM terminals and the static torque of the PMSM. The circuit model results are validated in two ways: (1) through experimental simulation, comparing the same results obtained using FEA and (2) through practical experimentation, producing a dynamic eccentricity in the machine of 0.3 mm. The results show that the proposed model is capable of accurately reproducing the behavior of the PMSM against eccentricity faults and presents computational time savings close to 99% compared to the response time obtained using FEA. This rapid PMSM model, parameterizable according to the degree of eccentricity, is the basis for the real-time simulation of the main machine waveforms, such as voltage, current and torque. Full article
(This article belongs to the Special Issue Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives)
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Review

Jump to: Research

27 pages, 4776 KiB  
Review
Technical Roadmaps of Electric Motor Technology for Next Generation Electric Vehicles
by Adil Usman and Anchal Saxena
Machines 2025, 13(2), 156; https://doi.org/10.3390/machines13020156 - 17 Feb 2025
Viewed by 1131
Abstract
This paper provides a consolidated discussion and proposes significant measures in improving and advancing the performance of synchronous machines employed in electric traction applications designed for passenger electric vehicles (EVs). The paper quantifies the discussion on improving the power density (kW/kg) and efficiency [...] Read more.
This paper provides a consolidated discussion and proposes significant measures in improving and advancing the performance of synchronous machines employed in electric traction applications designed for passenger electric vehicles (EVs). The paper quantifies the discussion on improving the power density (kW/kg) and efficiency (%η) of the machine with the commercially available solutions in terms of new design architectures, advanced emerging materials, and adoption of additive manufacturing (AM) technologies. New challenges and opportunities are identified for the optimized machine designs having the potential to meet the global standards while keeping the cost under control. This paper provides an overview of current trends, an introduction to innovative technologies, and changes in existing manufacturing practices to achieve high-performance electrical machines with improved fault tolerance capabilities and reliability. Thereby meeting the standards for the next generation of electric vehicles. Full article
(This article belongs to the Special Issue Fault Diagnostics and Fault Tolerance of Synchronous Electric Drives)
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