Residential Collegefalse
Status已發表Published
Hierarchical outlier detection approach for online distributed structural identification
Ke Huang; Ka-Veng Yuen
2020-08-10
Source PublicationStructural Control and Health Monitoring
ISSN1545-2255
Volume27Issue:11Pages:e2623
Other Abstract

In this paper, a hierarchical outlier detection approach is proposed for online distributed structural identification. In contrast to centralized identification, distributed identification extracts important features from the raw response data at the sensor nodes and transmits only them to the base station. Therefore, outlier detection is substantially more complicated than the traditional approach. In the proposed method, the local outliers in the raw data are detected directly at the corresponding sensor node, and they are excluded from further processing. However, if a sensor node is biased or exhibits other patterned outliers, these outliers will be undetectable at the sensor node level. It is necessary to conduct another level of outlier detection at the base station, namely, global outlier detection, before fusion. These two levels of outlier detection are of different nature. Local outlier detection concerns directly with the raw response data, whereas the targets of global outlier detection are the local estimation results of the stiffness parameters. Therefore, they require different mathematical tools. The proposed hierarchical outlier detection approach detects the local outliers according to the outlier probability of the data points at the sensor nodes, whereas it detects the global outliers according to the outlier probability of the local estimation results. By excluding both types of outliers, reliable online distributed structural identification can be achieved. Two examples are presented to demonstrate the proposed method.

KeywordBayesian Hierarchical Detection Online Distributed Identification Outlier Detection Structural Health Monitoring Wireless Sensor Network
DOI10.1002/stc.2623
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaConstruction & Building Technology ; Engineering ; Instruments & Instrumentation
WOS SubjectConstruction & Building Technology ; Engineering, Civil ; Instruments & Instrumentation
WOS IDWOS:000558347900001
PublisherJOHN WILEY & SONS LTD, THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND
Scopus ID2-s2.0-85089137827
Fulltext Access
FWCI0.4744502
Citation statistics
Document TypeJournal article
CollectionDEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Faculty of Science and Technology
THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Corresponding AuthorKa-Veng Yuen
AffiliationState Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering,University of Macau,999078,Macao
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Ke Huang,Ka-Veng Yuen. Hierarchical outlier detection approach for online distributed structural identification[J]. Structural Control and Health Monitoring,2020,27(11):e2623.
APA Ke Huang,&Ka-Veng Yuen.(2020).Hierarchical outlier detection approach for online distributed structural identification.Structural Control and Health Monitoring,27(11),e2623.
MLA Ke Huang,et al."Hierarchical outlier detection approach for online distributed structural identification".Structural Control and Health Monitoring 27.11(2020):e2623.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Ke Huang]'s Articles
[Ka-Veng Yuen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Ke Huang]'s Articles
[Ka-Veng Yuen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ke Huang]'s Articles
[Ka-Veng Yuen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.