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《Journal of Data Acquisition and Processing》 2011-05
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Application of Bridge Data Novelty Diagnosis Method for Expansion Joint Monitoring

Liang Dong1,Zhang Yufeng2,Yuan Shenfang1,Wu Jian1(1.The Aeronautic Key Laboratory of Smart Material and Structure,Nanjing University of Aeronautics & Astronautics,Nanjing,210016,China;2.Key Laboratory of Long-Span Bridge Health Inspection & Diagnosis Technology of Ministry of Transport,Nanjing,211112,China)  
The bridge data novelty diagnosis generated by the bridge structure damage or sensor fault,is a challenging problem for researchers in structural health monitoring area.A novel method has proposed which is based on a principal component analysis and hyperspherical one-class support vector machine toward the problem.Firstly,the original signal is pre-processed and denoised by wavelet threshold.Secondly,for the filtered signal,the statistical features in time domain,frequency domain and the autoregressive coefficients are used to construct its feature vector.Finally,the main feature vector generated from principal component analysis is presented to the hyperspherical one-class support vector machine for examination.The method is demonstrated on the monitoring data of an expansion joint in the Jiangyin bridge.It shows the accuracy and efficiency of the method for the identification of abnormal data.
【Fund】: 江苏省自然科学基金(BK2008510)资助项目;; 国家自然科学基金(50830201)资助项目;; 江苏省普通高校研究生科研创新计划(CX10B-097Z)资助项目
【CateGory Index】: U446
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