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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

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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.
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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:

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