Kernel-based PMP structure for nonlinear industrial quality-related process monitoring

Quality-related process monitoring as a supervised technology has increasingly attracted attention in complex industries. Various approaches have been studied to cope with this issue. Nevertheless, these methods cannot reasonably decompose the process variable space, resulting in deficiencies in monitoring quality-related faults. To handle this issue, this paper presents an orthogonal kernel partial least squares improved kernel least squares with a preprocessing-modeling-postprocessing (PMP) structure to implement quality-related process monitoring with more proper decomposition and more straightforward monitoring logic. Compared with the previous approaches, a nonlinear preprocessing technology is presented to eliminate the quality-unrelated knowledge of process variables, enormously enhancing the interpretability of modeling and improving the monitoring efficiency. Then, a proper decomposition is presented to decompose the kernel matrix into two orthogonal parts, significantly improving the monitoring performance. The theoretical analysis of the proposed method is provided in this paper. Finally, two cases indicate the validity and superiority of the proposed method.