论文标题
探索可扩展的，分布式的实时异常检测，以进行桥梁健康监测
Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring
论文作者
论文摘要
现代的实时结构健康监测系统可以生成大量信息，这些信息必须经过处理和评估，以检测早期异常并发出有关民用基础设施条件的及时警告和警报。如果必须从数千个建筑物中收集原始数据，那么当前基于云的解决方案就无法扩展。本文介绍了SHM系统的高效且可扩展的异常检测管道的全堆栈部署，该系统不需要将原始数据发送到云，而依赖于边缘计算。首先，我们基于异常检测的三种算法方法，即主成分分析（PCA），完全连接的自动编码器（FC-AE）和卷积自动编码器（C-AE）。然后，我们将它们部署在边缘传感器STM32L4上，具有有限的计算功能。我们的方法将网络流量降低了$ \ of cdot10^5 \ times $，从780kb/小时到单个安装的10字节/小时小于10个字节/小时，并最大程度地减少网络和云资源利用率，从而使监视基础架构的缩放。一项现实生活中的案例研究是意大利的一座公路桥梁，表明，将近传感器的计算结合了对异常检测算法，智能预处理和低功率广泛区域网络协议（LPWAN）的近传感器计算，我们可以大大降低数据通信和云计算成本，而对准确的检测准确性影响并未受到影响。
Modern real-time Structural Health Monitoring systems can generate a considerable amount of information that must be processed and evaluated for detecting early anomalies and generating prompt warnings and alarms about the civil infrastructure conditions. The current cloud-based solutions cannot scale if the raw data has to be collected from thousands of buildings. This paper presents a full-stack deployment of an efficient and scalable anomaly detection pipeline for SHM systems which does not require sending raw data to the cloud but relies on edge computation. First, we benchmark three algorithmic approaches of anomaly detection, i.e., Principal Component Analysis (PCA), Fully-Connected AutoEncoder (FC-AE), and Convolutional AutoEncoder (C-AE). Then, we deploy them on an edge-sensor, the STM32L4, with limited computing capabilities. Our approach decreases network traffic by $\approx8\cdot10^5\times$ , from 780KB/hour to less than 10 Bytes/hour for a single installation and minimize network and cloud resource utilization, enabling the scaling of the monitoring infrastructure. A real-life case study, a highway bridge in Italy, demonstrates that combining near-sensor computation of anomaly detection algorithms, smart pre-processing, and low-power wide-area network protocols (LPWAN) we can greatly reduce data communication and cloud computing costs, while anomaly detection accuracy is not adversely affected.
