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

TL;DR

The paper addresses reliable, long-horizon predictive maintenance in automated robotics by combining enhanced quantile regression with a two-stage QRNN pipeline and a gated temporal attention mechanism. The core innovation couples an Enhanced Quantile Regression Neural Network (EQRNN) with a Spiking Neural Network (SNN) layer, where a novel Gated Temporal Attention module dynamically selects relevant historical context across multiple time scales. Empirically, the approach achieves $92.3\%$ accuracy in predicting component failures with a $90$-hour advance warning, and field tests across $9$ robotic systems show a $94\%$ reduction in unexpected failures and a $76\%$ reduction in maintenance downtime. These results demonstrate a scalable, energy-aware, and interpretable framework suitable for Industry 4.0 manufacturing, capable of robust multi-sensor fusion and real-time anomaly detection at multiple forecasting horizons.

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.