Nonlinear Process Monitoring Using Regression and Reconstruction Method

In this paper, a new regression and reconstruction method for process monitoring is proposed. The main contributions of the proposed approaches are as follows: 1) a new nonlinear regression algorithm is proposed to extract the output-relevant variation, which, compared with the conventional algorithm, builds a more direct relationship between the input and output variables; 2) the fault direction is determined by possible fault magnitude of every possible principal component; and 3) the fault is effectively diagnosed compared with the conventional kernel partial least-squares (KPLS) method. The proposed method is applied to a continuous annealing process and is compared with the KPLS method. Experiment results show that the proposed method can more effectively detect fault compared with the KPLS method. In addition, the selection of fault direction is more accurate using the proposed reconstruction algorithm compared with the KPLS reconstruction approach.