Lifetime prediction of IGBT based on empirical wavelet transform combined with long short term memory network model

Abstract

The insulated gate bipolar transistor (IGBT) has the process of asymptotic and nonlinear degradation. It is of great significance to analyse its aging process and predict its remaining useful life for the safe and reliable operation of the power system. Therefore, this research introduces a method based on the Empirical Wavelet Transform combined with the Long Short Term Memory Network (EWT-LSTM) method to predict the remaining useful life of IGBT devices, to select the collector-emitter off instantaneous peak voltage as the characteristic signal of IGBT device aging. Firstly, the EWT method is used to decompose the aging signal. Then, in response to the problem of manually setting the number of segmentation layers in the EWT method, the adaptive spectrum segmentation method is used to adaptively determine the number of segmentation layers based on the signal to avoid interference caused by human participation. Finally, the LSTM time series model is used to predict each decomposed signal, and each prediction result is reconstructed to obtain the final prediction result. The results show that EWT-LSTM does not require noise filtering compared to other methods and has better prediction results, enabling better completion of IGBT life prediction work.