Please submit manuscripts in either of the following two submission systems

    ScholarOne Manuscripts

  • ScholarOne
  • 勤云稿件系统

  • 登录

Search by Issue

  • 2024 Vol.31
  • 2023 Vol.30
  • 2022 Vol.29
  • 2021 Vol.28
  • 2020 Vol.27
  • 2019 Vol.26
  • 2018 Vol.25
  • 2017 Vol.24
  • 2016 vol.23
  • 2015 vol.22
  • 2014 vol.21
  • 2013 vol.20
  • 2012 vol.19
  • 2011 vol.18
  • 2010 vol.17
  • 2009 vol.16
  • No.1
  • No.2

Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

期刊网站二维码
微信公众号二维码
Related citation:Xiaoling Liu,Qiao Huang,Yuan Ren.Anomaly Detection Algorithm for Stay Cable Monitoring Data Based on Data Fusion[J].Journal of Harbin Institute Of Technology(New Series),2016,23(3):39-43.DOI:10.11916/j.issn.1005-9113.2016.03.003.
【Print】   【HTML】   【PDF download】   View/Add Comment  Download reader   Close
←Previous|Next→ Back Issue    Advanced Search
This paper has been: browsed 1728times   downloaded 761times 本文二维码信息
码上扫一扫!
Shared by: Wechat More
Anomaly Detection Algorithm for Stay Cable Monitoring Data Based on Data Fusion
Author NameAffiliation
Xiaoling Liu School of Transportation, Southeast University, Nanjing 210096, China 
Qiao Huang School of Transportation, Southeast University, Nanjing 210096, China 
Yuan Ren School of Transportation, Southeast University, Nanjing 210096, China 
Abstract:
In order to improve the accuracy and consistency of data in health monitoring system, an anomaly detection algorithm for stay cables based on data fusion is proposed. The monitoring data of Nanjing No.3 Yangtze River Bridge is used as the basis of study. Firstly, an adaptive processing framework with feedback control is established based on the concept of data fusion. The data processing contains four steps: data specification, data cleaning, data conversion and data fusion. Data processing information offers feedback to the original data system, which further gives guidance for the sensor maintenance or replacement. Subsequently, the algorithm steps based on the continuous data distortion is investigated,which integrates the inspection data and the distribution test method. Finally, a group of cable force data is utilized as an example to verify the established framework and algorithm. Experimental results show that the proposed algorithm can achieve high detection accuracy, providing a valuable reference for other monitoring data processing.
Key words:  stay cable  health monitoring  anomaly detection  data fusion  manual inspection
DOI:10.11916/j.issn.1005-9113.2016.03.003
Clc Number:U448.27
Fund:

LINKS