6G数字孪生框架实现轴承故障实时检测，延迟低至0.8毫秒。
6G-Enabled Digital Twin Framework for Real-Time Cyber-Physical Systems: An Experimental Validation with Industrial Bearing Fault Detection
- 融合太赫兹通信与智能反射面，构建五层低延时架构
- 故障分类准确率97.7%，端到端延迟仅0.8毫秒
- 适合工业实时监控与预测性维护场景
当前集成数字孪生技术的网络物理系统在关键工业应用中难以实现实时性能。现有5G系统延迟超过10ms，无法满足自动驾驶控制与预测性维护等需亚毫秒响应的应用需求。本研究提出并验证了一种6G赋能的数字孪生框架，旨在实现物理资产与其数字镜像间的超低延迟通信与实时同步，聚焦于轴承故障检测这一关键工业场景。该框架整合太赫兹通信（0.1–1 THz）、智能反射面与边缘人工智能，采用五层架构。基于凯斯西储大学（CWRU）轴承数据集，实施15个时频域特征提取及随机森林分类算法。在分类准确率、端到端延迟和可扩展性等多维度对比传统WiFi-6与5G网络进行评估。系统达成97.7%的故障分类准确率，端到端延迟仅为0.8ms，较WiFi-6（12.5ms）提升15.6倍，较5G（4.2ms）提升5.25倍。系统展现优异可扩展性，处理时间增长接近线性，且在四种故障类别（正常、内圈、外圈、滚珠故障）上均保持稳定性能，宏平均F1得分超过97%。
原文摘要 · Abstract (English)
Current Cyber-Physical Systems (CPS) integrated with Digital Twin (DT) technology face critical limitations in achieving real-time performance for mission-critical industrial applications. Existing 5G-enabled systems suffer from latencies exceeding 10ms, which are inadequate for applications requiring sub-millisecond response times, such as autonomous industrial control and predictive maintenance. This research aims to develop and validate a 6G-enabled Digital Twin framework that achieves ultra-low latency communication and real-time synchronization between physical industrial assets and their digital counterparts, specifically targeting bearing fault detection as a critical industrial use case. The proposed framework integrates terahertz communications (0.1-1 THz), intelligent reflecting surfaces, and edge artificial intelligence within a five-layer architecture. Experimental validation was conducted using the Case Western Reserve University (CWRU) bearing dataset, implementing comprehensive feature extraction (15 time and frequency domain features) and Random Forest classification algorithms. The system performance was evaluated against traditional WiFi-6 and 5G networks across multiple metrics, including classification accuracy, end-to-end latency, and scalability. It achieved 97.7% fault classification accuracy with 0.8ms end-to-end latency, representing a 15.6x improvement over WiFi-6 (12.5ms) and 5.25x improvement over 5G (4.2ms) networks. The system demonstrated superior scalability with sub-linear processing time growth and maintained consistent performance across four bearing fault categories (normal, inner race, outer race, and ball faults) with macro-averaged F1-scores exceeding 97%.
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。