Alternative Diagnostic Approaches for Various Single-Fault Conditions in Direct-Drive Low-Speed Coreless Permanent Magnet Generators

A finite-element model of the direct-drive coreless permanent-magnet generator is used to simulate faults individually. Each fault case—rotor magnet demagnetization, a stator inter-turn short circuit, static eccentricity, and dynamic eccentricity—is introduced into the finite-element analysis (FEA) model separately, rather than in combination. For each isolated fault scenario, the stator current signals are processed using the Extended Park’s Vector Approach (EPVA) and the electromagnetic torque is examined in the frequency domain. The EPVA spectra and torque harmonics exhibit unique features for each fault type, allowing for clear discrimination among faults. These results demonstrate that modeling and analyzing faults one at a time yields distinct diagnostic signatures.

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Publication Details

Journal
Energies
Published
2025-11-13
DOI
https://doi.org/10.3390/en18225973
Citations
1
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
1.21

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Alternative Diagnostic Approaches for Various Single-Fault Conditions in Direct-Drive Low-Speed Coreless Permanent Magnet Generators

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article

Alternative Diagnostic Approaches for Various Single-Fault Conditions in Direct-Drive Low-Speed Coreless Permanent Magnet Generators

Konstantinos N. Gyftakis, Markus Mueller, Alexandros Sergakis, Marios Salinas, Nikolaos Gkiolekas
article en
1 citations

Abstract

A finite-element model of the direct-drive coreless permanent-magnet generator is used to simulate faults individually. Each fault case—rotor magnet demagnetization, a stator inter-turn short circuit, static eccentricity, and dynamic eccentricity—is introduced into the finite-element analysis (FEA) model separately, rather than in combination. For each isolated fault scenario, the stator current signals are processed using the Extended Park’s Vector Approach (EPVA) and the electromagnetic torque is examined in the frequency domain. The EPVA spectra and torque harmonics exhibit unique features for each fault type, allowing for clear discrimination among faults. These results demonstrate that modeling and analyzing faults one at a time yields distinct diagnostic signatures.

EnergiesVol. 18(22)
Technical University of Crete (GR), University of Edinburgh (GB)
UK Research and Innovation
Openalex Percentile: Top 15%
Machine Fault Diagnosis Techniques
1.21
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