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Proposes a knowledge-guided two-stage transfer learning framework using a lightweight GPT-2-style Transformer for cross-domain bearing fault diagnosis with limited data, achieving 92.61% accuracy with only 10% labeled data.
This paper proposes a quantum annealing enhanced Q-learning framework for remaining useful life prediction, using the D-Wave system to solve QUBO formulations for action selection. It outperforms classical and quantum baselines on NASA C-MAPSS and predictive maintenance datasets.
This paper introduces a lightweight approach for remaining useful life estimation using frozen embeddings from the Chronos-2 time-series foundation model combined with a simple regression head, achieving superior performance on industrial sensor data compared to baseline methods.
This paper proposes a semantic feature segmentation framework for predictive maintenance that decomposes monitoring signals into canonical and residual components to improve interpretability while maintaining predictive performance.
This paper introduces HEPA, a self-supervised architecture for predicting rare critical events in time series using a Joint-Embedding Predictive Architecture (JEPA) pretraining strategy. It demonstrates superior performance across multiple domains with significantly fewer labeled data and tuned parameters compared to leading models.