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Long Horizon Transformer Quantile Fault Prediction for Multi Site Industrial Predictive Maintenance
Long-horizon predictive maintenance requires models to distinguish slowly evolving degradation from normal operating-regime variation over planning windows measured in days rather than hours. This paper evaluates whether an explicit conditional-quantile representation provides an informative classifier interface for this problem. The proposed TQRNN30d framework combines a dual-stage quantile regression neural network (QRNN) feature extractor with a multi-stream temporal fusion classifier. Each hourly word of 81-channel machine behaviour is mapped to a 324-dimensional quantile-state representat
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-04T07:57:33.000Z
First collected: 2026-09-20T22:31:48.298Z. This is not the publication date.