Given the limitation of traditional univariate analysis method in processing the multicollinearity of dam monitoring data,this paper reconstructs the multivariate response variables by introducing principal component ...Given the limitation of traditional univariate analysis method in processing the multicollinearity of dam monitoring data,this paper reconstructs the multivariate response variables by introducing principal component analysis(PCA) method,explores the ways of determining principal components(PCs),and extracts a few PCs that have major influence on data variance.For steady observation series,a control field for the whole observation values has been established based upon PCA;for unsteady observation series that have significant tendency,a control field for the future observation values has been constructed according to PC statistical predication model.These methods have already been applied to an actual project and the results showed that data interpretation method with PCA can not only realize data reduction,lower data redundancy,and reduce noise and false alarm rate,but also be effective to data analysis,having a broad application prospect.展开更多
Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then p...Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then principal components are extracted through reconstructing multi effects. Moreover, combining with the optimal estimation theory, the method of singular value diagnosis in dam safety monitoring effect values is proposed. After dam monitoring information matrix is obtained, single effect state estimation matrix and multi effect fusion estimation matrix are constructed to make diagnosis on singular values to reduce false alarm rate. And the diagnosis index is calculated by PCA. These methods have already been applied to an actual project and the result shows the ability of the monitoring effect reflecting dam evolution behavior is improved as dam safety monitoring effect fusion estimation can take accurate identification on singular values and achieve data reduction, filter out noise and lower false alarm rate effectively.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos 50909041, 50879024, 50809025, 50539010, 50539110)the National Supporting Program (Grant Nos 2008BAB29B03, 2008BAB-29B06)the Natural Science Foundation of Hohai University (Grant No 2008426811)
文摘Given the limitation of traditional univariate analysis method in processing the multicollinearity of dam monitoring data,this paper reconstructs the multivariate response variables by introducing principal component analysis(PCA) method,explores the ways of determining principal components(PCs),and extracts a few PCs that have major influence on data variance.For steady observation series,a control field for the whole observation values has been established based upon PCA;for unsteady observation series that have significant tendency,a control field for the future observation values has been constructed according to PC statistical predication model.These methods have already been applied to an actual project and the results showed that data interpretation method with PCA can not only realize data reduction,lower data redundancy,and reduce noise and false alarm rate,but also be effective to data analysis,having a broad application prospect.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51079046, 50909041, 50809025, and 50879024)the National Science and Technology Support Plan (Grant Nos. 2008BAB29B03and 2008BAB29B06)+6 种基金the Special Fund of State Key Laboratory of China (Grant Nos. 2009586012, 2009586912, and 2010585212)the Fundamental Research Funds for the Central Universities (Grant Nos. 2009B08514, 2010B20414, 2010B01414, and 2010B14114)the China Hydropower Engineering Consulting Group Co. Science and Technology Support Pro-ject (Grant No. CHC-KJ-2007-02)Jiangsu Province "333 High-Level Personnel Training Project" (Grant No. 2017-B08037)Graduate Innovation Program of Universities in Jiangsu Province (Grant No. CX09B_ 163Z)Dominant Discipline Construction Program Funded Projects of University in Jiangsu ProvineScience Foundation for the Excellent Youth Scholars of Ministry of Education of China (Grant No. 20070294023)
文摘Based on the principal component analysis, principal components that have major influence on data variance are determined by the energy percentage method according to the correlation between monitoring effects. Then principal components are extracted through reconstructing multi effects. Moreover, combining with the optimal estimation theory, the method of singular value diagnosis in dam safety monitoring effect values is proposed. After dam monitoring information matrix is obtained, single effect state estimation matrix and multi effect fusion estimation matrix are constructed to make diagnosis on singular values to reduce false alarm rate. And the diagnosis index is calculated by PCA. These methods have already been applied to an actual project and the result shows the ability of the monitoring effect reflecting dam evolution behavior is improved as dam safety monitoring effect fusion estimation can take accurate identification on singular values and achieve data reduction, filter out noise and lower false alarm rate effectively.
