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.
Lightweight bidirectional long short- term memory based on automated model pruning with application to bearing remaining useful life prediction
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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.