A preprocessing pipeline for TCN-based RUL prediction on NASA C-MAPSS data yields higher accuracy than CNN, RNN, LSTM, and other neural baselines by focusing on data quality and continuous temporal modeling.
Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism
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A Novel Preprocessing-Driven Approach to Remaining Useful Life (RUL) Prediction Using Temporal Convolutional Networks (TCN)
A preprocessing pipeline for TCN-based RUL prediction on NASA C-MAPSS data yields higher accuracy than CNN, RNN, LSTM, and other neural baselines by focusing on data quality and continuous temporal modeling.