1 citations · 1 across the 5 of their papers we have counts for
5 papers
Quantile-Led Feature Extraction for Multi-Horizon Predictive Maintenance in Industrial Manufacturing Systems
David J Poland, Daniele Ravi, Na Helian
In data-driven predictive maintenance (PdM), feature extraction is usually treated as fixed preprocessing: a descriptor set is chosen once and reused while the downstream model or…
Long Horizon Transformer Quantile Fault Prediction for Multi Site Industrial Predictive Maintenance
David J Poland, Daniele Ravi, Na Helian
Long-horizon predictive maintenance requires models to distinguish slowly evolving degradation from normal operating-regime variation over planning windows measured in days rather…
Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics
David J Poland
This paper presents an integrative prognostic framework that combines Spatiotemporal Permutation Entropy (STPE), Boosted Enhanced Quantile Regression Neural Networks (B-EQRNNs), Ga…
Enhanced Quantile Regression with Spiking Neural Networks for Long-Term System Health Prognostics
David J Poland
This paper presents a novel predictive maintenance framework centered on Enhanced Quantile Regression Neural Networks EQRNNs, for anticipating system failures in industrial robotic…
Industrial Machines Health Prognosis using a Transformer-based Framework
David J Poland, Lemuel Puglisi, Daniele Ravi
This article introduces Transformer Quantile Regression Neural Networks (TQRNNs), a novel data-driven solution for real-time machine failure prediction in manufacturing contexts. O…