arXiv Machine Learning August 4, 2026 By Fatih \"Urgen, Do\u{g}ay Alt{\i}nel

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 27

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.

By Hao Yan, Ali Sarabi, Qing Zou, Boyang Xu

arXiv AI

Jun 11

Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation

arXiv:2606. 11990v1 Announce Type: cross Abstract: Remaining Useful Life (RUL) prediction is essential for industrial predictive maintenance, yet many learning-based approaches rely on extensive feature engineering or large labeled datasets to train task-specific sequence models.

By Amir El-Ghoussani, Michele De Vita, Ronald Naumann, Valiseios Belagiannis

llms rag

arXiv AI

Jun 2

Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights

arXiv:2507. 09766v2 Announce Type: replace-cross Abstract: Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM), supporting timely maintenance and dependable industrial operation.

By Mohamadreza Akbari Pour, Ali Ghasemzadeh, Mohamad Ali Bijarchi, Mohammad Behshad Shafii

reinforcement-learning benchmarks

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 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 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

Related stories

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

Time-Series Foundation Model Embeddings for Remaining Useful Life Estimation

Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights

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

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

Liquid Latent State Dynamics for Interpretable Turbofan Degradation Modeling