建筑结构学报2024，Vol.45Issue(3)：113-122,10.DOI:10.14006/j.jzjgxb.2023.0013
基于模式识别的结构健康监测异常数据诊断
Pattern recognition-based data anomaly detection for structural health monitoring
摘要
Abstract
Data anomaly is inevitable in field monitoring,leading to interference and misjudgment in the structural safety assessment.To address the problems of low efficiency and low accuracy in detecting multiple data anomalies in field monitoring,this study proposed a multiple data anomalies identification method based on feature extraction and pattern recognition neural network(PRNN).A set of features were established based on the characteristics of different data anomalies,transforming the long raw data samples into short feature vector samples,leading to significantly improved efficiency of data processing and anomaly detection.Moreover,the polarized AUCs curve was introduced to accurately describe the anomaly detection performance,improving in the optimization efficiency for the feature selection and the adjustment of network parameters.A structural health monitoring system was built on the Wuhan Yangtze Shipping Center(335 m).The accuracy and efficiency of the proposed method were verified using the monitoring data of the super high-rise building.The results show that six types of data anomalies are recognized with a 99.7％detection accuracy using the PRNN-based data anomaly detection method,and the operation time is only one-tenth of the time of deep learning methods.关键词
结构健康监测/异常数据检测/模式识别神经网络/特征提取/极坐标化AUCs曲线Key words
structural health monitoring/data anomaly detection/pattern recognition neural network/feature extraction/polarized AUCs curve分类
建筑与水利引用本文复制引用
高珂,翁顺,陈志丹,朱宏平,夏勇..基于模式识别的结构健康监测异常数据诊断[J].建筑结构学报,2024,45(3):113-122,10.基金项目
国家重点研发计划(2021YFF0501001,2023YFC3805700),国家自然科学基金项目(52308315),华中科技大学交叉研究支持计划(2023JCYJ014),中国博士后科学基金(2023M731206). （2021YFF0501001,2023YFC3805700）
