← Back to all news
arXiv Machine Learning By Davide Frizzo, Francesco Borsatti, Gian Antonio Susto

Uncertainty and Business-Aware Remaining Useful Life Estimation for Semiconductor Manufacturing

Read the original on arXiv Machine Learning →

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.

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 Machine Learning.

Related stories

arXiv AI
Sep 7

Long Horizon Transformer Quantile Fault Prediction for Multi Site Industrial Predictive Maintenance

The paper introduces TQRNN30d, a long‑horizon predictive maintenance model that uses a dual‑stage quantile regression neural network to transform hourly machine data into a 324‑dimensional quantile‑state representation, which is then classified with a multi‑stream temporal fusion architecture. Trained on data from 72 machines across nine facilities, the model achieves high performance at 30‑day horizons (F1 ≈ 80%, recall ≈ 80%, precision ≈ 82%, accuracy ≈ 82%, ROC‑AUC ≈ 0.82) and outperforms 18 baseline methods at 7‑, 14‑, and 30‑day thresholds. The study demonstrates that explicit conditional‑quantile representations can effectively distinguish gradual degradation from normal operation over multi‑day planning windows, though generalisation to unseen sites or equipment remains untested.

By David J Poland, Daniele Ravi, Na Helian
llmsmultimodal
More like this →
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
More like this →
arXiv AI
Sep 10

Quantile-Led Feature Extraction for Multi-Horizon Predictive Maintenance in Industrial Manufacturing Systems

The paper introduces a quantile‑led feature‑extraction framework for predictive maintenance that separates representation learning from downstream modeling. Using a dual‑stage MLP‑QRNN hierarchy, it learns a ten‑quantile distribution per sensor and refines a mid‑tail quantile set into compact, channel‑resolved features. Experiments on 72 machines across nine facilities show that increasing the retained mid‑tail quantiles improves short‑term F1 scores, and that horizon‑conditioned extractors outperform a fixed short‑horizon extractor, demonstrating that feature extraction should be horizon‑dependent rather than fixed preprocessing.

By David J Poland, Daniele Ravi, Na Helian
ragbenchmarks
More like this →
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
More like this →