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.
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.
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.
The paper investigates data‑driven predictive maintenance where health‑condition estimation is coupled with an ordering‑replacement decision model. It shows that the traditional Estimate‑Then‑Optimize (ETO) approach can lead to inconsistent, suboptimal decisions and introduces an Integrated Estimate‑Optimize (IEO) framework that directly optimizes maintenance cost, bypassing conventional accuracy metrics. Experiments on CMAPSS data demonstrate that IEO reduces mean maintenance cost by 14.8–22.7% versus ETO and 7.2–11.8% versus a decision‑oriented baseline, while also lowering failure rates and cost variability, even when prediction errors are higher.
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.
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.
arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.
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:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient 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.
The paper presents a predictive maintenance framework for semiconductor manufacturing that combines deep learning sequence models with simultaneous quantile regression to provide uncertainty‑aware remaining useful life (RUL) estimates. It evaluates several architectures—including state‑space models—on ion‑milling data from the 2018 PHM Data Challenge, showing that the Diagonal State Space (S4D) model delivers the most accurate RUL predictions across quantiles. Compared to preventive maintenance baselines, the S4D approach significantly reduces business costs by avoiding unnecessarily early interventions.
arXiv:2605.10822v2 Announce Type: replace Abstract: Cyber-physical system (CPS) forecasting models depend on sensor streams with noisy, biased, missing, or temporally misaligned readings, yet standar...
The paper investigates how treating the optimization objective as a design variable can improve Remaining Useful Life (RUL) prediction in predictive maintenance. Five model architectures are compared under single‑objective and multi‑objective hyperparameter optimization, with the latter using NSGA‑II and Entropy‑CRITIC weighting to balance accuracy and prediction timeliness. Results on NASA C‑MAPSS and BackBlaze datasets show that multi‑objective optimization reduces directional imbalance in predictions and can alter model rankings, highlighting the importance of objective choice in RUL modeling.