PG-TMT couples a physics-aligned tri-branch encoder with EVT-calibrated decision rules to achieve higher PR-AUC and shorter detection times at controlled false-alarm rates across multiple bearing datasets.
Remaining use- ful life estimation—A review on the statistical data-driven ap- proaches
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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CNN-LSTM model predicts nine functional variables with uncertainty estimates for an angle grinder and integrates finite-element fatigue analysis to produce reliability trajectories for reuse decisions.
citing papers explorer
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Physics-Guided Tiny-Mamba Transformer for Reliability-Aware Early Fault Warning
PG-TMT couples a physics-aligned tri-branch encoder with EVT-calibrated decision rules to achieve higher PR-AUC and shorter detection times at controlled false-alarm rates across multiple bearing datasets.
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Uncertainty Aware Functional Behavior Prediction and Material Fatigue Assessment for Circular Factory
CNN-LSTM model predicts nine functional variables with uncertainty estimates for an angle grinder and integrates finite-element fatigue analysis to produce reliability trajectories for reuse decisions.