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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.
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.