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Reliability and Condition Monitoring of Electric Motors and Drives: 2nd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 25 May 2026 | Viewed by 731

Special Issue Editors


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Guest Editor
Department of Engineering and Applied Sciences, University of Bergamo, 24129 Bergamo, Italy
Interests: rotating electrical machines; fault-tolerant design and control; lifetime modelling; partial discharge; reliability-oriented design; thermal management
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Engineering and Applied Sciences, University of Bergamo, 24129 Bergamo, Italy
Interests: electrical machines; diagnostics of electrical machines; predictive maintenance; motor current signature analysis; stray flux analysis; vibration analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing demand for more energy-efficient technologies is acting as a driving force in the development of lightweight and high-power density electric drives intended for applications ranging from industry to the transport sector. Nevertheless, failures occurring in electric drive components (i.e., motor, power converter, battery) might lead to significant consequences in terms of economic losses and down-times. Therefore, condition-monitoring technology, fault-tolerant design, and enhanced reliability represent viable solutions for preventing and minimizing failure outcomes.

This Special Issue intends to collect original research, practical contributions, and review articles on the condition monitoring and fault‑tolerant design of electrical machines and drives. Research studies on insulation aging mechanisms, lifetime prediction, and partial discharge are also invited.

Topics of interest include but are not limited to:

  • Condition monitoring and signal processing;
  • Fault-tolerant electrical machines and drives;
  • Advanced control algorithms for improving reliability;
  • Modelling, detection, and measurement of partial discharge;
  • Accelerated aging tests on electrical machine insulation;
  • Insulation lifetime modelling and prediction;
  • Prediction and diagnostics of inter-turn short circuits, bearing faults, broken rotor bars, eccentricity, and manufacturing defects.

Dr. Paolo Giangrande
Dr. Marcello Minervini
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 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. Energies is an international peer-reviewed open access semimonthly 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 2600 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

  • condition monitoring
  • fault tolerance
  • dielectric breakdown
  • partial discharge
  • diagnostics
  • lifetime forecasting

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Published Papers (1 paper)

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Review

45 pages, 3086 KB  
Review
Modelling of Insulation Thermal Ageing: Historical Evolution from Fundamental Chemistry Towards Becoming an Electrical Machine Design Tool
by Antonis Theofanous, Israr Ullah, Michael Galea, Paolo Giangrande, Vincenzo Madonna, Yatai Ji, John Licari and Maurice Apap
Energies 2025, 18(23), 6087; https://doi.org/10.3390/en18236087 - 21 Nov 2025
Viewed by 504
Abstract
Electrical insulation systems (EISs) are the principal reliability bottleneck of modern electrical machines (EMs). Among the many stresses acting on insulation, thermal stress is the most pervasive because it accelerates chemical reactions that progressively erode dielectric and mechanical integrity, ultimately dictating service life. [...] Read more.
Electrical insulation systems (EISs) are the principal reliability bottleneck of modern electrical machines (EMs). Among the many stresses acting on insulation, thermal stress is the most pervasive because it accelerates chemical reactions that progressively erode dielectric and mechanical integrity, ultimately dictating service life. As EMs migrate into compact, high-power-density platforms—automotive, aerospace, and industrial drives—designers need lifetime models that are not merely explanatory but actionable, linking operating temperatures and missions to quantified ageing and risk. This review article traces the evolution of thermal-ageing modelling from fundamental chemistry to a practical design tool. The historical empirical lineage of Arrhenius equation, Arrhenius–Dakin model, and Montsinger model is first revisited, clarifying their assumptions, parameter definitions, and the construction of thermal endurance curves. A discussion then follows on extensions that address deviations from first-order kinetics and demonstrate how variable temperature histories can be incorporated through cumulative damage formulations suitable for duty-cycle analysis. Since models are required to be anchored in data, accelerated thermal ageing (ATA) practices on representative specimens are outlined, alongside a description of the Weibull post-processing for deriving percentile lifetimes aligned with design targets. Building upon these foundations, the Physics-of-Failure (PoF) approach is introduced as a reliability-oriented design (ROD) methodology, in which validated lifetime models guide material selection and geometry optimisation while supporting prognostics and health management during operation. The emerging trend towards a hybrid PoF–AI approach is also discussed, which integrates artificial intelligence to identify nonlinear degradation patterns and drifting parameter relationships beyond the reach of empirical models, with physical constraints ensuring that predictions remain consistent with known ageing mechanisms. Such integration enables the learning process to adapt to operational variability and coupled stress effects, thereby improving both the accuracy and physical interpretability of lifetime estimation. The review aims to provide a concise view of models, tests, and workflows that convert thermal-ageing knowledge into robust, design-time decisions. By linking empirical and physics-based insights with modern data-driven learning, these developments support proactive maintenance, sustainable asset management, and extended operational lifetimes for next-generation EMs. Full article
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