Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring

Modern real-time structural health monitoring (SHM) 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 article 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 approximate to 8 . 10(5)x, from 780 kB/h to less than 10 Bytes/h 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 preprocessing, 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.

Automated summary

This article introduces an efficient and scalable anomaly detection pipeline for structural health monitoring systems that relies on edge computation rather than cloud computing, reducing network traffic and minimizing resource utilization. A real-life case study on an Italian highway bridge demonstrates the successful application of the approach in reducing data communication and cloud computing costs without compromising anomaly detection accuracy.