# Enhanced Quantile Regression with Spiking Neural Networks for Long-Term System Health Prognostics

arXiv: https://arxiv.org/abs/2501.05087 · HTML: https://mlsanity.com/paper/2501.05087/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2025.

## Built on by (most cited first, 1 of 1)

- [Boosted Enhanced Quantile Regression Neural Networks with Spatiotemporal Permutation Entropy for Complex System Prognostics](https://mlsanity.com/paper/2507.14194/) (2025)

## Abstract (arXiv)

This paper presents a novel predictive maintenance framework centered on Enhanced Quantile Regression Neural Networks EQRNNs, for anticipating system failures in industrial robotics. We address the challenge of early failure detection through a hybrid approach that combines advanced neural architectures. The system leverages dual computational stages: first implementing an EQRNN optimized for processing multi-sensor data streams including vibration, thermal, and power signatures, followed by an integrated Spiking Neural Network SNN, layer that enables microsecond-level response times. This architecture achieves notable accuracy rates of 92.3% in component failure prediction with a 90-hour advance warning window. Field testing conducted on an industrial scale with 50 robotic systems demonstrates significant operational improvements, yielding a 94% decrease in unexpected system failures and 76% reduction in maintenance-related downtimes. The framework's effectiveness in processing complex, multi-modal sensor data while maintaining computational efficiency validates its applicability for Industry 4.0 manufacturing environments.