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

This study proposes a hybrid fault detection methodology for detecting epoxy faults in hairpin-based 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.