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

David J Poland

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arXiv · 2501.05087

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

David J Poland

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Abstract

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.

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David J Poland. 2025-01-09. Enhanced Quantile Regression with Spiking Neural Networks for Long-Term System Health Prognostics. https://arxiv.org/abs/2501.05087

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