arXiv AI June 11, 2026 By Amir El-Ghoussani, Michele De Vita, Ronald Naumann, Valiseios Belagiannis

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 17

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

Aug 28

FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation

FedCMAPSS is a new benchmark for federated learning applied to remaining useful life (RUL) estimation, built on the NASA C‑MAPSS dataset. It defines five standardized tasks that mimic real‑world industrial scenarios, from ideal IID conditions to highly heterogeneous data distributions. The paper evaluates state‑of‑the‑art federated optimization algorithms across multiple neural architectures, providing reproducible baselines and publicly available code and data splits.

By Amelia Sorrenti, Matteo Pennisi, Concetto Spampinato, Simone Palazzo

benchmarks

arXiv Machine Learning

Jun 11

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.

By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han

llms benchmarks

arXiv Machine Learning

Sep 22

Benchmarking Hybrid Deep Learning Architectures for Predictive Maintenance in Industry 4.0

The study evaluates six deep learning architectures for predictive maintenance in Industry 4.0, focusing on Recurrent Neural Networks (RNNs) and Transformers. It finds that Transformers perform well on stable, slow-moving data but overreact to noisy, chaotic data, whereas a hybrid model combining an LSTM layer with a Transformer layer better filters noise and delivers more consistent predictions. The hybrid approach improves accuracy and reliability across varying levels of data volatility.

By Zhengyang (Cissy), Gu, Joseph E. Hernandez, Thomas Cook, John Burtenshaw, Sean Scott, Chris Couch

llms benchmarks

arXiv Machine Learning

Sep 22

Time Series Foundation Models for Process Model Forecasting

arXiv:2512.07624v2 Announce Type: replace Abstract: Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of...

By Yongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt

fine-tuning efficiency benchmarks

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

One email a morning, machine-written

Related stories

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

FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

Benchmarking Hybrid Deep Learning Architectures for Predictive Maintenance in Industry 4.0

Time Series Foundation Models for Process Model Forecasting

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