Lifetime prediction of power MOSFET based on LSTM with successive variational mode decomposition and error compensation

Accurate prediction of the remaining useful life (RUL) of metal oxide semiconductor field effect transistors (MOSFETs) is the key to safe and reliable operation of power electronics. In this paper, we combine long short-term memory (LSTM) networks with successive variational mode decomposition (SVMD) and use error compensation methods to build a lifetime prediction model, which improves the performance of the prediction model by reducing the interaction between different sequences and using error sequence compensation. The results show that, compared with the Bayesian optimized LSTM, the method has the advantages of high prediction accuracy and low prediction uncertainty.