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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.