To make efficient use of data in high-dimensional space and eliminate the influence of redundant dimension on the final result

this paper presents a method of dimension reduction using Copula function and LU(forward substitution) decomposition method

in which the Copula function provides a suitable correlation model to compare the multivariate distribution data and the LU decomposition method can quickly obtain the linear combination of correlation coefficient. Then

through the correlation coefficient analysis and elimination of irrelevant or redundant data and other linear combinations of variables

the integrity of the data and information can be maintained

so feature selection can be carried out for the multi-dimensional data. Moreover

the above method and the principal component analysis(PCA)

the simple principal component analysis(SPCA) as well as the singular value decomposition(SVD) feature selection method were applied to the processing and analysis of a large amount of data of three kinds of turbine operating conditions in a factory within 2 months. The results show that the method is better than the other methods in reducing the number of identified faulty data and enhancing the identifying efficiency.