Data anomaly identification method based on local outlier factor and application in monitoring data of heritage building structure

被引：0
|
作者：
Yang N. [1 ,2 ]
Fu Y. [1 ,2 ]
Li T. [1 ,2 ]
机构：
[1] School of Civil Engineering, Beijing Jiaotong University, Beijing
[2] Beijing’s Key Laboratory of Structural Wind Engineering and Urban Wind Environment, Beijing Jiaotong University, Beijing
来源：
Jianzhu Jiegou Xuebao/Journal of Building Structures | 2022年 / 43卷 / 10期
基金：
中国国家自然科学基金;
关键词：
data cleaning; heritage building structure; local outlier; monitoring data;
D O I：
10.14006/j.jzjgxb.2022.0046
中图分类号：
学科分类号：
摘要：
In-depth mining of monitoring data information based on structural health monitoring is an important means to obtain the health status of heritage buildings and ensure their durability and safety. In order to accurately carry out data analysis and structural safety assessment, as well as to distinguish the abnormal hardware, artificial disturbance or environmental mutation two kinds of data anomaly genesis, respectively define two types of data anomalies according to different causes, this paper defines two kinds of data anomalies of heritage building masonry structures. According to the characteristics of long-term static and slow change of monitoring data of heritage building masonry structures, an improved outlier identification algorithm based on density is proposed to improve the quality of monitoring data cleaning. Through compression and segmentation of time series data and transformation of series data, local outliers in data can be accurately picked up. Through qualitative and quantitative analysis, the improved outlier recognition algorithm based on local outlier density has high accuracy and efficiency in identifying local outliers of monitoring data of heritage building masonry structures, which can solve the problem that the existing data outlier identification algorithm is not suitable. © 2022 Science Press. All rights reserved.
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页码：68 / 75
页数：7
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