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.
Deep learning-based residual useful lifetime prediction for assets with uncertain failure modes
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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.