arXiv Machine Learning

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.

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

arXiv AI

Jun 24

AI-Driven Predictive Maintenance with Environmental Context Integration for Connected Vehicles: Simulation, Benchmarking, and Field Validation

arXiv:2603. 13343v3 Announce Type: replace-cross Abstract: Predictive maintenance for connected vehicles offers the potential to reduce unexpected breakdowns and improve fleet reliability, but most existing systems rely exclusively on internal diagnostic signals and are validated on simulated or industrial benchmark data.

By Kushal Khemani (Independent Researcher, India), Anjum Nazir Qureshi (Rajiv Gandhi College of Engineering Research,Technology)

fine-tuning benchmarks

arXiv AI

Aug 10

Beyond Foundation Models: Dimension-Aware Neural Architecture Search with Small-Data Representation Models for Cryocooler Lifetime Prediction

arXiv:2608. 06993v1 Announce Type: cross Abstract: Large-scale pretrained time-series models achieve strong results through large-scale pretraining and task-agnostic representation learning, but they rely on abundant, diverse data that industrial and scientific domains often lack.

By Gregor Molan (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Grafika Jati (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Francesco Barchi (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Andrea Acquaviva (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Alja\v{z} Osterman (LE-Tehnika d.o.o., \v{S}uceva 27, Kranj, 4000, Slovenia), Martin Molan (Comtrade AI GmbH, Grafenauweg 8, Zug, 6300, Switzerland)

llms safety

arXiv Machine Learning

Jul 21

Leakage-Robust Evaluation and Data-Scale Sensitivity of Attention-Enhanced Multi-Task Learning for Joint Fault Diagnosis and Remaining Useful Life Estimation

arXiv:2607. 16493v1 Announce Type: new Abstract: Multi-task deep learning models that jointly perform fault classification and remaining useful life (RUL) regression are increasingly used in predictive maintenance, yet reported performance can be strongly affected by how sliding-window sequences are split into training and test sets.

By Md Mahamudur Rahaman Shamim, Md. Nuruzzaman, Zannatul Ferdus, Md Rajib Ahmed, Abieer Nwshad Anward, Mohammad Tooneer, Johir Uddin Khan, Khalid Hossen

benchmarks

arXiv Machine Learning

Jun 30

An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling

arXiv:2606. 29117v1 Announce Type: cross Abstract: Post-hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization.

By Hooman Torkaman, Ellis Oti Boateng, Jignesh Solanki, Anurag Srivastava

llms benchmarks

arXiv Machine Learning

Jun 19

SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with Application to GPU Data

arXiv:2606. 20451v1 Announce Type: cross Abstract: Competing risks are commonly observed in engineering fields and can bring challenges to time-to-event data modeling when the application scenarios are complicated.

By Jie Min, Yueyao Wang, Mengkun Chen

arXiv AI

Aug 6

Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity

arXiv:2608. 04045v1 Announce Type: cross Abstract: Federated learning (FL) enables aircraft fleet operators to jointly train remaining-useful-life (RUL) models from engine sensor telemetry without sharing raw data.

By Chinmoy Mitra, Md. Mehedi Hasan Nipu, Mohammad Sakib Mahmood, Md. Rakibul Islam, M. F. Mridha

benchmarks safety

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