智能传感助力桥梁安全：从传感器信号到AI驱动的异常检测
Smart Sensing for Safer Bridges: From Sensor Signals to AI-Driven Anomaly Detection
- Smart Sensor Systems AS（智能传感器系统公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文利用信号处理与孤立森林机器学习模型对挪威桥梁传感器数据进行异常检测，通过多种指标评估并验证了机器学习方法的有效性。
AI中文摘要：
桥梁对交通连通性和城市发展具有重要贡献。因此，可靠的桥梁监测对于保护公共安全以及检测桥梁传感器数据中的异常行为至关重要，这些异常行为可能为异常结构状况提供早期指示。本文采用两种互补的方法研究真实世界桥梁传感器数据中的异常检测，即信号处理和数据驱动的机器学习模型孤立森林。实时桥梁传感器数据采集自安装在挪威一座桥梁上的iBridge传感器设备。使用异常计数、异常检测时间、处理速率、异常率、可视化和时间一致性对方法进行评估。此外，进行了受控的异常注入分析以评估每种方法的敏感性。数值结果展示了不同的检测特征和计算需求，凸显了机器学习（特别是数据驱动的孤立森林）与信号处理相结合在识别桥梁传感器测量异常方面的潜力。
英文摘要：
Bridges contribute significantly to transportation connectivity and urban development. Therefore, reliable bridge monitoring is crucial for protecting public safety and detecting anomalous behavior in bridge sensor data that may provide early indications of abnormal structural conditions. This paper investigates anomaly detection in real-world bridge sensor data using two different complementary approaches, namely signal processing and the data-driven machine learning model Isolation Forest. The real-time bridge sensor data is collected from an iBridge sensor device installed on a bridge in Norway. The methods are evaluated using anomaly counts, anomaly detection time, processing rate, anomaly rates, visualization, and temporal agreement. Moreover, a controlled anomaly-injection analysis is performed to evaluate the sensitivity of each method. Numerical results demonstrate distinct detection characteristics and computational requirements, highlighting the potential of machine learning, particularly the data-driven Isolation Forest, alongside signal processing for identifying anomalies in bridge sensor measurements.
