Improved IGBT Aging Diagnosis for Three-Phase Inverters via Phase-Angle Feature Redesign and Kernel SHAP Analysis

초록

This study proposes an improved artificial neural network (ANN)-based framework for diagnosing the aging state and predicting the remaining useful life (RUL) of Insulated Gate Bipolar Transistor (IGBT) modules in three-phase inverter systems. Building upon a prior 15,625-scenario simulation dataset, the proposed approach fundamentally redesigns the input feature space by replacing the three-phase voltage rates of change with phase angles of symmetrical components (∠V0,∠V1,∠V2), eliminating redundant information and enabling accurate estimation of the inverter aging severity. Two independent ANN models—a classification model for risk-level identification and a regression model for continuous aging index estimation—share the same 6-dimensional input space derived from symmetrical component transformation. The classification model achieves 96.86% accuracy under noisy conditions (SNR 20 dB), and the regression model attains R2 = 0.9028, both representing improvements over the prior study under their respective diagnostic frameworks. Kernel SHAP global sensitivity analysis, cross-validated against an ablation ground truth, shows that gradient-based attribution fails to capture the positive-sequence (V1) contribution (0.00% gradient vs. 18.5%/41.1% SHAP) under multicollinearity among inputs (r = 0.853 between V0 and V2).A4-input ablation experiment empirically validates the necessity of all six features, with classification accuracy dropping to 35–89% and regression R2 collapsing to −8.10 upon V1 removal. The proposed software-based solution requires no additional hardware and is broadly applicable to predictive maintenance in electric vehicles, drones, and renewable energy systems. © 2013 IEEE.

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