A multi-head attention fusion network integrates monotonic degradation trends, discrete operating state embeddings from clustering, and residual noise using BiLSTM and attention mechanisms to improve prognostic accuracy under varying conditions on NASA data.
Remaining useful life estimation in prognostics using deep convolution neural networks
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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.
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A Multi-head Attention Fusion Network for Industrial Prognostics under Discrete Operational Conditions
A multi-head attention fusion network integrates monotonic degradation trends, discrete operating state embeddings from clustering, and residual noise using BiLSTM and attention mechanisms to improve prognostic accuracy under varying conditions on NASA data.
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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.