IGBT lifetime prediction based on EMD-LSTM

Abstract

Aiming at the problem of fatigue failure caused by the cyclic impact of thermal stress and electrical stress during IGBT operation, a long short-term memory (LSTM) network life prediction method based on EMD (Empirical Mode Decomposition) decomposition is proposed. This experiment uses the accelerated aging data set provided by the NASA PCoE laboratory, analyse and select the collector-emitter transient spike voltage as the failure characteristic parameter, and use the EMD algorithm to decompose the original time series into multiple relatively stable and with different characteristic scales the eigenmode function and trend term reduce the complexity of the time series. Through the LSTM network prediction model, the decomposed eigenmode function components and trend items are respectively predicted, and then the prediction results are superimposed to obtain the result. The results show that the prediction accuracy of the EMD-LSTM model is higher, and it can better realize the life prediction of IGBT, and it also has certain reference value for the life prediction of other power electronic devices.