Author

Daniele Ravì

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#artificial intelligence Preprint Sep 2026

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

The results show that representations do not transfer reliably beyond their design horizon unless feature capacity, temporal embedding, activation strategy, and sensor breadth are scaled with the forecasting task, and the framework supports treating PdM feature extraction as a horizon-dependent representational stage r...

David J. Poland, Daniele Ravì, Na Helian · 0 citations

#artificial intelligence Preprint Sep 2026

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

The proposed TQRNN30d framework combines a dual-stage quantile regression neural network (QRNN) feature extractor with a multi-stream temporal fusion classifier, which supports held-out-machine performance within the observed homogeneous nine-facility fleet, but does not establish unseen-site, cross-equipment, or cross...

David J. Poland, Daniele Ravì, Na Helian · 0 citations