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- W4387269504 abstract "Aiming at the fatigue failure problem caused by the cycling influence of electrical stress and thermal stress of IGBT during working state, this paper presents a remaining useful life (RUL) prediction method based on Snake Optimizer (SO) to optimize the stacked bidirectional long short-term memory (Bi-LSTM) neural network model. Firstly, the peak voltage of collector-emitter in the IGBT aging data provided by NASA data center is selected as the characteristic parameter, and the wavelet transformation is used to preprocess the data. Secondly, the Bi-LSTM neural network model is constructed and the attention mechanism is introduced to calculate the allocation weight of the time series, which enhances the expression ability of the nonlinear features of the hidden layer, and adopts SO to optimize the BiALSTM neural network training hyperparameters and construct the SO-Bi-ALSTM neural network prediction model. Finally, the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean squared error (RMSE) are selected as the evaluation criteria, and the prediction accuracy of extreme learning machine (ELM), Transformer, LSTM and SO-Bi-ALSTM models is compared and analyzed. The results show that the MAE predicted by the SO-Bi-ALSTM model is 0.0173, the MAPE is 0.0921%, and the RMSE is 0.0160, which indicates the prediction performance of SO-Bi-ALSTM is better than the other methods." @default.
- W4387269504 created "2023-10-03" @default.
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- W4387269504 date "2023-08-12" @default.
- W4387269504 modified "2023-10-06" @default.
- W4387269504 title "Remaining useful life prediction for IGBT based on SO-Bi- ALSTM" @default.
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- W4387269504 doi "https://doi.org/10.1109/ccis59572.2023.10263071" @default.
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