arXiv AI

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.

arXiv Machine Learning
Aug 18

Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks

arXiv:2608. 14764v1 Announce Type: new Abstract: With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical.

By Bego\~na Ispizua, Serio Gil-L\'opez, Leire Arrizabalaga, Ibai La\~na
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
arXiv Machine Learning
5d ago

Learning coarse-step dynamics and internal mechanical response with graph networks

The paper introduces Newmark‑eta‑DGN, a graph neural network framework that learns coarse‑step dynamics and internal mechanical responses from discretely sampled trajectories. It combines a semi‑implicit update inspired by the Newmark‑eta method with an operator‑weighted virtual hub to capture system‑wide coupling. The model can predict long‑horizon motion and infer unobserved forces and stiffness operators across deformable beams, human gait, and protein dynamics without explicit supervision on mechanical quantities.

By Vinay Sharma, Olga Fink
arXiv Machine Learning
Aug 14

History-informed Lagrangian Neural Networks

arXiv:2608. 13215v1 Announce Type: new Abstract: Forecasting the long-horizon evolution of mechanical systems from position-only observations is a pivotal yet difficult task, as hidden velocities and trajectory-specific physical properties must be inferred simultaneously.

By Tianshuo Zhang, Xianglei Xing, Wenzhe Zhai, Jia Gao, He Cao
arXiv Machine Learning
Aug 10

Defining Energy Indicators for Impact Identification on Aerospace Composites: A Structured Feature Selection Approach Guided by Domain Knowledge

arXiv:2511. 01592v2 Announce Type: replace Abstract: Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface.

By Nat\'alia Ribeiro Marinho, Richard Loendersloot, Frank Grooteman, Jan Willem Wiegman, Uraz Odyurt, Tiedo Tinga
arXiv AI
Jul 22

Physics-Guided Masked Multi-Task Network for Edge-Friendly Battery Health Diagnostics from Sto-chastically Fragmented Charging Profiles

arXiv:2607. 18330v1 Announce Type: cross Abstract: The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity.

By Shuhao Chen, Tianyu Shi, Chengyi Tu