arXiv Machine Learning

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

arXiv:2607. 22268v1 Announce Type: cross Abstract: Remaining useful life (RUL) prediction and failure-mode classification are central tasks in predictive maintenance.

arXiv Machine Learning

Jul 21

SurvCF(t): Counterfactual Explanations for Survival Analysis in Predictive Maintenance Multivariate Time Series Data

arXiv:2607. 16969v1 Announce Type: new Abstract: Predictive maintenance relies on accurate Remaining Useful Life estimation, often formulated using survival analysis over multivariate time-series data.

By Zara Karazian, Panagiotis Papapetrou, Sindri Magn\'usson, Erik Frisk, Tony Lindgren

efficiency benchmarks safety

arXiv Machine Learning

Aug 4

Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines

arXiv:2608. 01819v1 Announce Type: new Abstract: To improve the operational readiness of combat aircraft engines and reduce unplanned maintenance costs, accurately estimating the remaining useful life (RUL) is critical.

By Fatih \"Urgen, Do\u{g}ay Alt{\i}nel

arXiv Machine Learning

Jul 28

Generalization bounds and sample complexity for remaining useful life prediction from complete degradation trajectories

arXiv:2607. 23454v1 Announce Type: new Abstract: Data-driven remaining useful life (RUL) prediction requires complete degradation trajectories for training, yet such run-to-failure data are scarce and expensive.

By Huy Hoang Le, Kim-Anh Nguyen

benchmarks safety data-engineering

arXiv Machine Learning

Jul 21

Tabular Foundation Models Can Do Survival Analysis

arXiv:2601. 22259v2 Announce Type: replace Abstract: While tabular foundation models have achieved remarkable success in classification and regression, adapting them to model time-to-event outcomes for survival analysis is non-trivial due to right-censoring, where data observations may end before the event of interest occurs.

By Da In Kim, Wei Siang Lai, Kelly W. Zhang

llms

arXiv AI

Jun 2

Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations

arXiv:2606. 01894v1 Announce Type: new Abstract: Accurate Remaining Useful Life prediction is critical for industrial predictive maintenance.

By Deyu Zhuang, Peiliang Gong, Yang Shao, Liyuan Shu, Qi Zhu, Xiaoli Li, Daoqiang Zhang

rag benchmarks safety

arXiv Machine Learning

Jul 3

Liquid Latent State Dynamics for Interpretable Turbofan Degradation Modeling

arXiv:2607. 01986v1 Announce Type: new Abstract: Multivariate time-series models for prognostics are often evaluated by point prediction accuracy, yet their internal states rarely expose a coherent degradation process.

By Weizhi Nie, Weijie Wang, Yuting Su

benchmarks

arXiv AI

2d ago

PEFT-MuTS: A Multivariate Parameter-Efficient Fine-Tuning Framework for Remaining Useful Life Prediction based on Cross-domain Time Series Representation Model

arXiv:2601. 22631v2 Announce Type: replace-cross Abstract: The application of data-driven remaining useful life (RUL) prediction has long been constrained by the availability of large amount of degradation data.

By En Fu, Yanyan Hu, Zengwang Jin, Kaixiang Peng

fine-tuning

arXiv Machine Learning

Jul 23

CruiseBench: A Real-Flight-Aligned N-CMAPSS Benchmark for Engine RUL Prediction

arXiv:2607. 19380v1 Announce Type: new Abstract: Remaining useful life (RUL) prediction estimates how long an engine can continue safe operation and is central to maintenance planning.

By Pu Cheng, Qiang Miao

benchmarks

arXiv AI

Jul 16

Uncertainty-Aware Sequential Decision Rules for Event-Triggered LLM Invocation in Streaming Systems

arXiv:2607. 13048v1 Announce Type: cross Abstract: Streaming inference pipelines increasingly pair lightweight fast models with Large Language Models (LLMs) that provide rich semantic understanding at substantial cost.

By Zhaohui Wang

llms

arXiv Machine Learning

Jun 4

SurvPFN: Towards Foundation Models for Survival Predictions

arXiv:2606. 04564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched.

By Samuel B\"ohm (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany), Lennart Purucker (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Frank Hutter (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Pascal Schlosser (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, US, CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany)

arXiv Machine Learning

Aug 4

The Label Defines the Timescale: Trait-State Limits of Temporal-Aggregate Learning

arXiv:2608. 01587v1 Announce Type: cross Abstract: Machine-learning benchmarks often pair a label that aggregates a long temporal horizon with input observed through one or a few short windows.

By Xizhe Zhang

benchmarks

arXiv Machine Learning

Jun 5

Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models

arXiv:2606. 05481v1 Announce Type: new Abstract: Data-driven Prognostics and Health Management (PHM) uses time-varying condition-monitoring data to diagnose system states and estimate remaining useful life in engineered assets.

By Raffael Theiler, Lev Telyatnikov, Leandro Von Krannichfeldt, Olga Fink

llms

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