桥梁健康监测系统的异常数据特征库构建

摘要

For the actual abnormal data identification and diagnosis of bridge health monitoring system, the abnormal feature library construction method is presented. Firstly, the monitoring data are denoised and extracted to the signal features in time domain, frequency domain and AR model. Then, using KPCA feature extraction method, abnormal data feature library is built, including short-circuit, beat-wave and ship-collision. Finally, the abnormal state of bridge monitoring system is identified by BP neural network to verify the effectiveness of feature library construction. The vibration data analysis for Jiangyin Yangtze River Bridge shows that the method can be used to diagnose abnormalities.%针对实际桥梁健康监测系统的数据异常识别和诊断问题,提出了一种异常数据特征库的构建方法.首先,对监测数据进行去噪处理,选取统计信号的时域、频域以及AR模型系数特征；然后,利用核主元分析的方法进行特征提取,建立包括传感器短路、拍波和船撞等异常数据的特征库；最后,通过BP神经网络方法识别桥梁监测系统的异常状态,以验证特征库构建的有效性.江阴长江大桥主梁振动监测数据的分析表明,桥梁异常数据特征库构建方法能够用于诊断桥梁振动.