Ex-situ Inspection of Concentrated Stator Coils by Means of Impedance Spectroscopy

Panagiotou, Panagiotis A.; Lambourne, Alexis; Jewell, G.W.

doi:10.1109/icem51905.2022.9910588

Cited by 9 publications

(2 citation statements)

References 44 publications

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“…Stator samples 1 to 5 with normal conditions are categorized as "healthy" whereas stators 6 to 10 exhibit different types of fault conditions. Specifically, stators 6-8 possess faults related to the epoxy and stators 9 and 10 carry faults related to welding, as shown in The characteristics of stator windings have been found to be reflected in impedance measured at high frequencies [52], [53], [54]. In this study, the high-frequency impedance data are acquired by applying an instrument called GAMRY TM [55], which measures the impedance using a sweeping sinusoidal signal from low to high frequencies.…”

Section: Appendix a Experimental Setupmentioning

confidence: 99%

A Hybrid Fault Detection Method for Hairpin Windings Integrating Physics Model and Machine Learning

Zhang,

Huangfu,

Ziada

et al. 2024

IEEE Access

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This study proposes a hybrid fault detection methodology for detecting epoxy faults in hairpinbased stator windings of electric motors. The hybrid methodology integrates a model-based approach for feature extraction and a data-driven approach for fault classification. A new lumped-parameter equivalent circuit model specifically for hairpin windings is developed. It can accurately simulate the high-frequency impedance behaviors of hairpin windings and physically interpret the distinction of the measurement curves under different epoxy configurations. Using system identification, the parameters of this new model are identified to extract the features of phase windings, reflecting different fault conditions by varying the parameters in distinct ranges. Fault classification is implemented using a data-driven method to distinguish the underlying patterns, which is difficult to achieve by conventional threshold limit checking due to the inevitably introduced noise and uncertainties. Principal Component Analysis (PCA) is applied to refine the features, followed by a Support Vector Machine (SVM) performing fault classification. The proposed hybrid methodology successfully detects epoxy-related fault conditions, providing a new strategy for fault detection.

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Section: Appendix a Experimental Setupmentioning

confidence: 99%

A Hybrid Fault Detection Method for Hairpin Windings Integrating Physics Model and Machine Learning

Zhang,

Huangfu,

Ziada

et al. 2024

IEEE Access

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“…Most research [3], [4] collect vibration and current signals for fault detection of electric motor components, such as bearings, rotors, and stators. For stator windings, electrical impedance has been found to reveal the characteristics [5]- [7]. However, only impedance spectroscopes are explored and the specific relationship between impedance and stator winding characteristics is not revealed.…”

Section: Introductionmentioning

confidence: 99%

Efficient Hairpin Winding Fault Detection Using Impedance Measurements

Zhang,

Huangfu,

Ziada

et al. 2023

IEEE Access

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This study investigates various hairpin winding faults in electric motors using impedance measurements. Both high-frequency and low-frequency impedances are measured to characterize winding fault conditions. This study proposes various techniques to extract distinctive feature patterns that are associated with fault conditions. These include open circuit faults that show significant discrimination within the low-frequency range, and welding mismatch faults that are distinguished by a proposed similarity indicator. Insulation faultsfaults that are related to epoxyare found to be more difficult to diagnose using simple statistical metrics, so this study proposes a machine learning classification model using a support vector machine (SVM). The results show that the SVM model achieves a high accuracy using a small number of training samples. The methods discussed provide cost-efficient solutions to effectively detect welding and insulation faults, ensuring the product quality of hairpin windings.

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An Investigation of the Impedance Spectroscopy Suitability for Traction Motor Windings Insulation Health Assessment

Chronopoulos,

Glykos,

Gyftakis

2024

2024 International Conference on Electrical Machines (ICEM)

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Ex-situ Inspection of Concentrated Stator Coils by Means of Impedance Spectroscopy

Cited by 9 publications

References 44 publications

A Hybrid Fault Detection Method for Hairpin Windings Integrating Physics Model and Machine Learning

A Hybrid Fault Detection Method for Hairpin Windings Integrating Physics Model and Machine Learning

Efficient Hairpin Winding Fault Detection Using Impedance Measurements

An Investigation of the Impedance Spectroscopy Suitability for Traction Motor Windings Insulation Health Assessment

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