利用脉冲神经网络增强分位数回归以实现长期系统健康预测
Enhanced Quantile Regression with Spiking Neural Networks for Long-Term System Health Prognostics
AI总结：
提出融合 EQRNN 与 SNN 的双阶段预测性维护框架，利用多模态传感器数据提前 90 小时预测工业机器人故障，并在现场测试中显著降低故障与停机时间。
摘要：
本文提出了一种以增强分位数回归神经网络（Enhanced Quantile Regression Neural Networks，EQRNN）为核心的新型预测性维护框架，用于预测工业机器人中的系统故障。我们通过结合先进神经架构的混合方法来应对早期故障检测挑战。该系统采用双计算阶段：首先实现一个经过优化的 EQRNN，用于处理包括振动、热和功率特征在内的多传感器数据流；随后接入一个集成的脉冲神经网络（Spiking Neural Network，SNN）层，以实现微秒级响应时间。该架构在部件故障预测中达到 92.3% 的显著准确率，并具有 90 小时的提前预警窗口。在工业规模上对 50 个机器人系统进行的现场测试表明，其运营改进显著：意外系统故障减少 94%，与维护相关的停机时间减少 76%。该框架在保持计算效率的同时处理复杂多模态传感器数据的有效性，验证了其适用于工业 4.0 制造环境。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
