Remaining Available Life Prediction Model for Power Electronic Devices Based on Temporal Neural Networks

Existing research methods have not been able to accurately predict the aging failure of power electronic equipment, we propose an accuracy improvement method, the goal of this study is to predict the age group and RUL of power electronic equipment more accurately. To fill the gap of existing models, this paper proposes a remaining life prediction model based on Long Short-Term Memory (LSTM) network, which is chosen as the central element of time-series neural network because of its ability to effectively capture long-term dependencies in long-term series. Using the optimization of traditional LSTM model, we determine and propose adaptive gating mechanism improved model which dynamically adjusts learning strategy of the network by device state change, and enhances the model fitting ability for nonlinear recession process. Furthermore, we develop a multilayer LSTM network and use a hybrid loss function by integrating mean square error (MSE) with absolute error (MAE) to enhance the predictive performance of the model in different failure periods. The experimental results demonstrate that the improved LSTM model has higher accuracy and stronger robustness than the traditional LSTM and other benchmark models in the RUL prediction task of multiple power electronic devices.