A knowledge-driven method for IGBT remaining useful life prediction using bidirectional learning and physics-enhanced pathformer networks

Abstract As the core component responsible for high-frequency power switching in photovoltaic inverters, accurately predicting the remaining useful life (RUL) of insulated gate bipolar transistors (IGBTs) has become a key factor in ensuring the stable operation of photovoltaic systems. However, existing methods struggle to precisely characterize the degradation characteristics and processes of IGBTs at different time points. To address these issues, this paper proposes a MIG-PI-Pathformer (Multi-stage Inverse Gaussian Physical Information Pathformer Network) RUL prediction method that integrates physical degradation models with deep learning. This method establishes a multi-stage Inverse Gaussian degradation model based on the physical failure mechanisms of IGBTs and couples it with the dual attention mechanism of the Pathformer model to capture complex degradation features, adaptively divide time scales, and thereby correct prediction errors in the physical model. Additionally, physical rule constraints are incorporated into the Pathformer loss function to ensure that RUL predictions align with degradation mechanisms. Simulation results show that, on NASA’s IGBT aging dataset, compared to the single Pathformer, the proposed method reduces mean square error and mean absolute error by 70.21$\%$ and 17.84$\%$, respectively, and improves $R^2$ by 7.66$\%$. This method provides more accurate and physically interpretable technical support for fault warning and optimized maintenance of photovoltaic inverters.