Efficient Hairpin Winding Fault Detection Using Impedance Measurements

Abstract

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 faults – faults that are related to epoxy – are 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.

Keywords

• Hairpin windings
• fault detection
• signal-based
• machine learning