An IGBT coupling structure with a smart service life reliability predictor using active learning

An effective approach is proposed to evaluate the service life reliability of a multi-physics coupling structure of an insulated gate bipolar transistor (IGBT) module. The node-based smoothed finite element method with stabilization terms is firstly employed to construct an electrical-thermal-mechanical (ETM) coupling structure of the IGBT module, based on which the multi-physics responses can be accurately calculated to predict the service life of the IGBT module. By using the high-quality sample data obtained through the ETM coupling model, a Monte Carlo based active learning Kriging metamodel (AK-MCS) is developed to assess the service life reliability of the IGBT module, which can greatly reduce the computational cost needed by the surrogate model construction and reliability analysis. Numerical results show that the proposed ETM coupling structure can produce high-quality sample data of the IGBT dynamics and the AK-MCS machine learning technique can accurately estimate the service life reliability of the IGBT module.