Monitoring Method of Non-Gaussian Process Based on Fractal Analysis With Kernel Independent Component Regression

Abstract

Process monitoring is very important for the safety of industrial production processes. The traditional monitoring method based on independent component analysis has the disadvantages as follows. (1) The importance ordering problem of independent components has not been solved; (2) The dynamic problem is not considered. To address these issues, a fractal dimension-based dynamic kernel independent components regression (FD-KICR) method is proposed. The contributions of the proposed method are as follows: (1) The intrinsic dimension of the data is calculated through the improved fractal dimension, and then the number of selected nonlinear ICs (nICs) is determined; (2) The time lags are computed with fractal dimension to effectively describe the dynamic structure of data; (3) By correlating temperature with independent components selection and indirectly monitoring temperature changes through bands of independent components, this method can effectively monitor the safety of the production process. This proposed method is applied to the electrical fused magnesia furnace (EFMF). The experience results show the effectiveness of this method.