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

• URL: http://arxiv.org/abs/2607.22268v1
• Date: Fri, 24 Jul 2026 13:04:32 GMT
• Title: General Value Functions for Remaining Useful Life and Failure-Mode Prediction
• Abstract summary: We formulate vector General Value Function (GVF) prediction on an absorbing degradation process.<n>We estimate RUL and failure-mode probabilities as temporally consistent targets rather than independent window-level labels.<n>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.
• Score: 5.709180757030779
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• Beyond Optimal Rates in Stochastic Optimization: Trajectory-Adaptive Stopping Rules [42.38093055717409]
  We develop new confidence-sequence techniques and a general time-uniform empirical Bernstein inequality for adapted processes.<n>We show that the resulting stopping rules can require several orders of fewer than natural gradient horizons.
  arXiv  Detail & Related papers  (2026-08-26T09:02:46Z)
• Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories [0.8870188183999853]
  We propose Doubly Robust Functional Representation Learning (DR-FRL) to turn irregular histories into estimand-targeted states for observed-history regimes.<n>DR-FRL is a cross-fitted workflow that turns irregular histories into estimand-targeted states for observed-history regimes.<n>A VitalDB audit shows that DR-FRL can use irregular laboratory point clouds and deliver a useful negative finding.
  arXiv  Detail & Related papers  (2026-07-30T17:33:42Z)
• ScoreStop: Gradient-based early stopping using functional score tests [0.0]
  We propose a gradient-based early-stopping rule that casts the stopping decision at each iteration as a test of the null hypothesis that the current predictor is the population risk minimizer.<n>Because our test uses gradients rather than loss values, the same construction applies to implicit validation losses such as LambdaRank, and data-dependent losses such as Cox regression via influence functions.
  arXiv  Detail & Related papers  (2026-06-01T18:05:22Z)
• Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagation [4.891998873486944]
  We propose a unified learning framework that trains neural networks to produce context-aware Gaussian overbounds.<n>We provide a scoped analysis of discrete-to-continuous conservatism and compact-domain objective regularity.<n>The framework is modality-agnostic and applicable to learning systems that require conservative, feature-conditioned uncertainty estimates.
  arXiv  Detail & Related papers  (2026-05-15T09:47:05Z)
• Distributional Spectral Diagnostics for Localizing Grokking Transitions [4.480801209867367]
  In grokking, a model first fits the training data while test accuracy remains low, and only later begins to generalize.<n>We ask whether this transition can be localized from observed training trajectories before the test accuracy rises, and formulate grokking transition localization as a diagnostic problem with an explicit threshold/FPR/lead-time trade-off.
  arXiv  Detail & Related papers  (2026-05-07T03:08:58Z)
• Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection [1.7751300245073598]
  AxonAD is an unsupervised detector that treats multi-head attention query evolution as a short horizon predictable process.<n>A tail-aggregated query mismatch score measures cosine deviation between predicted and target queries on recent timesteps.<n>On in-vehicle telemetry with interval annotations, AxonAD improves ranking quality and temporal localization over strong baselines.
  arXiv  Detail & Related papers  (2026-03-13T11:40:51Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Error-quantified Conformal Inference for Time Series [55.11926160774831]
  Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data.<n>We propose itError-quantified Conformal Inference (ECI) by smoothing the quantile loss function.<n>ECI can achieve valid miscoverage control and output tighter prediction sets than other baselines.
  arXiv  Detail & Related papers  (2025-02-02T15:02:36Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes [70.80716221080118]
  The paper takes a generative perspective on policy evaluation via temporal-difference (TD) learning. The OS-GPTD approach is developed to estimate the value function for a given policy by observing a sequence of state-reward pairs. To alleviate the limited expressiveness associated with a single fixed kernel, a weighted ensemble (E) of GP priors is employed to yield an alternative scheme.
  arXiv  Detail & Related papers  (2021-12-01T23:15:09Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.