用脉冲神经网络提升长期设备健康预测精度，提前90小时预警故障。
Enhanced Quantile Regression with Spiking Neural Networks for Long-Term System Health Prognostics
- 融合增强分位数回归与脉冲神经网络，处理多传感器数据流。
- 故障预测准确率达92.3%，可提前90小时预警，实测故障率降94%。
- 适合工业4.0场景，微秒级响应，兼顾精度与计算效率。
本文提出一种新型预测性维护框架——增强分位数回归神经网络（EQRNN），用于工业机器人系统故障的长期健康预测。针对早期故障检测难题，采用混合架构：先通过优化的EQRNN处理振动、温度、功率等多源传感器数据，再接入集成脉冲神经网络（SNN）层，实现微秒级响应。该系统在50台工业机器人上进行实地测试，故障预测准确率达到92.3%，可提供90小时的提前预警窗口。实测显示，意外故障减少94%，维护停机时间降低76%。该框架在处理复杂多模态数据的同时保持高效计算，验证了其在工业4.0制造环境中的适用性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。