首页|基于模式识别的结构健康监测异常数据诊断

基于模式识别的结构健康监测异常数据诊断

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
实际结构监测中不可避免出现异常数据,干扰结构的安全评估并引起误判.针对实际监测中多类型异常数据检测效率低和检测结果不准确的问题,提出一种基于特征提取和模式识别神经网络(PRNN)的多类型异常数据识别方法.针对不同类型异常数据的特点建立特征指标集合,通过特征提取将冗长原始样本转化为简短特征向量,显著提高了数据处理和异常检测的效率;进一步引入极坐标化AUCs曲线对多种异常的识别效果进行精确描述,提高了特征指标选取和网络参数调节的优化效率.建立武汉长江航运中心(335 m)健康监测系统,采用该超高层建筑的监测数据对所提方法的精度和效率予以验征.结果表明,基于特征提取和PRNN的多类型异常数据识别方法对6种数据异常的识别准确率达到99.7％,且运算时长仅为深度学习方法的数十分之一.
Pattern recognition-based data anomaly detection for structural health monitoring
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.

structural health monitoringdata anomaly detectionpattern recognition neural networkfeature extractionpolarized AUCs curve

高珂、翁顺、陈志丹、朱宏平、夏勇

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华中科技大学土木与水利学院,湖北武汉 430074

华中科技大学控制结构湖北省重点实验室,湖北武汉 430074

国家数字建造技术创新中心,湖北武汉 430074

香港理工大学土木与环境工程系,香港 999077

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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

国家重点研发计划国家重点研发计划国家自然科学基金项目华中科技大学交叉研究支持计划中国博士后科学基金

2021YFF05010012023YFC3805700523083152023JCYJ0142023M731206

2024

10.14006/j.jzjgxb.2023.0013

建筑结构学报
中国建筑学会

建筑结构学报

CSTPCD北大核心
影响因子：1.546
ISSN：1000-6869
年,卷(期)：2024.45(3)
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