土木工程与管理学报2026，Vol.43Issue(4)：12-21,10.DOI:10.13579/j.cnki.2095-0985.2026.20250398
基于深度学习的斜拉桥速度传感器信号异常检测方法研究
Research on Signal Anomaly Detection Method of Cable-stayed Bridge Speed Sensor Based on Deep Learning
摘要
Abstract
The rapid development of sensor technology has made it possible to conduct comprehensive and continuous health monitoring of cable-stayed bridges.However,in practical engineering applica-tions,sensors are inevitably affected by various factors,leading to potential abnormal fluctuations or systematic biases in the collected data,which may result in abnormal fluctuations or systematic devia-tions in the collected data.Therefore,developing reliable sensor signal anomaly detection method has important theoretical significance and engineering value for the safe operation of bridges.This paper generates five common types of sensor faults based on normal speed signal data collected by DH5907N to simulate different types of fault signals.Specifically,these types of faults include offset,drift,accu-racy degradation,gain variation,and complete failure.When creating the dataset,80％of the collected speed sensor signal data(including normal and faulty)will be used for network training,and the remai-ning 20％will be extracted for network testing.Comparing the model proposed in this paper with Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)model,and considering various fault fu-sion scenarios.The experimental results show that the deep learning method constructed in this paper has high accuracy in detecting anomalies in speed sensor signals and good noise resistance.关键词
斜拉桥/速度传感器/深度学习/信号异常检测Key words
cable-stayed bridge/speed sensor/deep learning/signal anomaly detection分类
交通工程引用本文复制引用
黄伟,凌小宝,程锦辉,史晓贞..基于深度学习的斜拉桥速度传感器信号异常检测方法研究[J].土木工程与管理学报,2026,43(4):12-21,10.基金项目
国家自然科学基金(52678407) （52678407）
湖北省自然科学基金创新群体项目(2024AFA006) （2024AFA006）
