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

arXiv: https://arxiv.org/abs/2607.22268 · HTML: https://mlsanity.com/paper/2607.22268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [On the Statistical Benefits of Temporal Difference Learning](https://mlsanity.com/paper/2301.13289/) (2023)
- [The surprising efficiency of temporal difference learning for rare event prediction](https://mlsanity.com/paper/2405.17638/) (2024)
- [Degradation Modeling and Prognostic Analysis Under Unknown Failure Modes](https://mlsanity.com/paper/2402.19294/) (2024)
- [Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes](https://mlsanity.com/paper/1903.03867/) (2019)

## Abstract (arXiv)

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 temporally consistent targets rather than independent window-level labels, and estimate them with a multi-step temporal-difference estimator, TD(n,λ). Supporting theory identifies the Bellman fixed point of the vector GVFs, characterizes the linear projected-TD limit and its relation to complete-return Monte Carlo regression under realizability, and explains when bootstrapped TD targets are less variable than Monte Carlo returns. On an event-triggered multimode simulation and NASA C-MAPSS label-scarce stitch data, TD improves RUL and failure-mode prediction relative to a supervised same-backbone Monte Carlo control, especially under scarce complete labels. Practically, fragmented, identity-free degradation records can contribute local Bellman transitions instead of being discarded until complete run-to-failure labels are available.