A novel GRU-augmented time-frequency estimator for IGBT remaining useful life prediction

Abstract

Aging-induced failure of Insulated Gate Bipolar Transistors (IGBTs) significantly restricts the reliability of power electronic systems. Accurate and efficient prediction of IGBT Remaining Useful Life (RUL) is critical for proactive risk mitigation and ensuring system stability. Despite numerous existing aging models and data-driven methodologies, maintaining prediction robustness and accuracy under diverse and complex operational scenarios remains challenging. To overcome these limitations, we introduce a novel GRU-Augmented Time-Frequency Estimator (GATE) tailored for IGBT lifetime prediction. GATE utilizes an autoregressive time-series prediction framework trained via the Teacher Forcing strategy to recursively decode the electrical parameters indicative of the IGBT’s aging state from rich time-frequency features. Experimental validations are performed using the square-wave power cycling dataset from the NASA Prognostics Data Repository. The results demonstrate that GATE significantly enhances prediction accuracy, reducing Mean Squared Error (MSE) to 0.0026 and Mean Absolute Error (MAE) to 0.045, representing improvements of 38.1% and 19.6%, respectively, compared to the leading baseline method. Moreover, recursive forecasting experiments show that GATE precisely predicts the remaining power cycles until the aging threshold (defined as a 15% increase in Vce(on)) at various aging stages (10–60%). Ablation analyses further underline the critical contribution of the frequency-domain component. Collectively, these findings underscore GATE’s capability to reliably decode IGBT RUL directly from historical operational data, bypassing intricate electrical or mechanical modeling, thereby offering a practically deployable and broadly generalizable solution for lifetime management in power electronic devices.