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.
URL https://www.sciencedirect.com/science/article/pii/S0888327015002034
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes bearing-wise data partitioning to remove leakage in ML bearing fault diagnosis, reformulates as multi-label classification, and shows training bearing count drives generalization on four public datasets.
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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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Towards a more realistic evaluation of machine learning models for bearing fault diagnosis
Proposes bearing-wise data partitioning to remove leakage in ML bearing fault diagnosis, reformulates as multi-label classification, and shows training bearing count drives generalization on four public datasets.