Abstract: Insulated gate bipolar transistor(IGBT) device is a kind of core technology component which is widely used in industry,communication,computer,automotive electronics,and other fields.It is very important to improve the safety of the device.Recently,the prediction of the life of IGBT devices by machine learning has become a hot research issue.However,the common neural network prediction still has problems of long training time and low accuracy.In order to solve these problems,a machine learning model based on Stacking multi-model fusion is proposed to realize IGBT life prediction.The model effectively improves the accuracy and efficiency of prediction.The algorithm consists of a two-layer structure,which incorporates four complementary machine learning algorithm models.In the first layer,the mild gradient lift tree model(LGBM),extreme gradient lift tree model(XGBoost),and ridge regression model are used to predict the IGBT life,and then the prediction results are input into the second layer for training.The second layer uses a linear regression model and the final IGBT life is predicted by the two-layer model training.Through the comparison of experimental data,it is confirmed that the machine learning model based on Stacking multi-model fusion is superior to the commonly used long and short-term memory neural network(LSTM) algorithm model,its MSE of IGBT life prediction is 93% lower than that of the LSTM algorithm model.What's more,the average time of model training is reduced to 13% of the LSTM algorithm model.

Key words: IGBT device, Life prediction, Long short-term memory, Machine learning, Stacking algorithm