General Value Functions for Remaining Useful Life and Failure-Mode Prediction

arXiv stat.ML5d4 min read

arXiv:2607.22268v1 Announce Type: new Abstract: Remaining useful life (RUL) prediction and failure-mode classification are central tasks in predictive maintenance. Many data-driven pipelines use fixed-window supervised learning with complete terminal labels; such routes do not naturally encode the temporal recursion linking successive degradation-state predictions when observations are partial or unit identities are unavailable. We formulate prognostics as vector General Value Function (GVF) prediction on an absorbing degradation process, treating RUL and failure-mode probabilities as temporal

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General Value Functions for Remaining Useful Life and Failure-Mode Prediction

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