Study of feature extraction method of multi-information source for continuous casting process parameters

Abstract

The control of the roll gap of the segment is one of the key links to ensure the quality of cast billet. In this paper, the big data in traditional continuous casting production operations is studied through in-depth experimental comparative analysis of linear and nonlinear dimension reduction method. The method is suitable for continuous casting to obtain the data of the dimension reduction. The method of principal component analysis is improved by using standardized data increment method. A faster and more efficient method of dimension reduction is obtained when the unrelated data, training time and reconstruction error are removed. Actual data simulation results show that this method is more efficient and suitable for continuous casting than any other dimension reduction method.