A Remaining Useful Life Prediction Method for Insulated Gate Bipolar Transistors Based on Transfer Learning

Insulated Gate Bipolar Transistor (IGBT) is recognized as having its Remaining Useful Life (RUL) prediction constitute a crucial component in the implementation of Prognostics and Health Management (PHM). The investigation of IGBT RUL under varying operating conditions is considered to possess significant theoretical importance and engineering value. A transfer learning-based RUL prediction method is proposed in this study. Initially, corresponding current and voltage signals are extracted from aging data according to the operational characteristics of IGBTs, from which the on-state resistance is calculated and characterized. Subsequently, features from both source and target domain data are systematically selected and fused to construct a health indicator with high transferability. Finally, a Maximum Mean Discrepancy-based Domain Adversarial Neural Network (MMD-DANN) is employed to minimize cross-domain discrepancies, thereby enabling high-accuracy RUL prediction to be achieved across different operating conditions. Experimental validation demonstrates that the proposed method is proven to provide an effective solution for reliability assessment of power devices under different operational conditions.