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
arXiv:2507. 14194v3 Announce Type: replace-cross Abstract: This paper presents an integrative prognostic framework that combines Spatiotemporal Permutation Entropy (STPE), Boosted Enhanced Quantile Regression Neural Networks (B-EQRNNs), Gated Temporal Attention, a Spiking Neural Network (SNN) refinement stage, and a Temporal Fusion Transformer (TFT) classifier.
By David J Poland
arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.
By Sarah Al-Shareeda, Gulcihan Ozdemir, Heung Seok Jeon
arXiv:2609. 08375v1 Announce Type: cross Abstract: Industrial process monitoring is fundamental to the safety and economic performance of modern process plants.
By Liang Cao, Weide Liu, Yan Qin, Jun Cheng, Weisi Lin, Bhushan Gopaluni
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
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
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
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
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.
By Davide Frizzo, Francesco Borsatti, Gian Antonio Susto
The paper introduces FreqCondNorm, a Transformer-based architecture that adds a frequency-conditioned normalization layer to unify heterogeneous time-series data for predictive maintenance. The model is pretrained on five public datasets using masked auto‑encoding and contrastive learning, achieving 99.2% accuracy on CWRU and 82.1% zero‑shot accuracy on MFPT, showing strong transfer across sampling frequencies. However, it does not improve remaining useful life prediction, indicating a mismatch between pretraining and RUL objectives that requires further study.
By Zaynab Raounak, Camille LHermine, Zhiguo Zeng
arXiv:2607. 02632v1 Announce Type: cross Abstract: Time-series forecasting supports decisions in finance, en-ergy, transportation, public health, and industrial monitoring.
By Shah Nawaz Haider, Steve Austin, Arnab Barua, Sarowar Morshed Shawon, Hadaate Ullah
arXiv:2606. 24459v1 Announce Type: new Abstract: Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultaneously in industrial environments.
By Jinghan Wang, Feng Cheng, Wentao Wu, Hang Li, Gaoliang Peng, Tianchen Liu