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This paper proposes a framework for applying tabular foundation models to industrial time series for prognostics and health management, demonstrating strong performance and data efficiency across multiple PHM tasks.
This paper benchmarks five uncertainty quantification methods for neural network predictions of turbine gas temperature, evaluating trade-offs in coverage, width, and stability to guide prognostics and health management in engines.