A General Quality-Related Nonlinear Process Monitoring Approach Based on Input–Output Kernel PLS
Xiangyu Kong, Jiayu Luo, Xiaowei Feng, Meizhi Liu
Abstract
Projection to latent structure (PLS) is a well-known data-based approach widely used in industrial process monitoring. Kernel PLS (KPLS) was proposed in prior studies to apply the PLS in the nonlinear process. However, KPLS-based methods only consider the nonlinear variation of the input and ignore that of the input and output simultaneously. Once the nonlinearity lies in inputs and outputs, the KPLS-based methods cannot accurately describe the nonlinear feature, and result in missing alarms. To provide a common monitoring approach for various nonlinear cases, an input–output KPLS (IO-KPLS) model is proposed. The proposed IO-KPLS maps both the original input and output variables into a high-dimensional space. A new nonlinear objective function is then established to extract latent variables. In addition, a nonlinear regression is designed to construct the IO-KPLS model. By constructing statistics, a complete quality-related process monitoring strategy is designed. Driven by the proposed strategy, the nonlinear feature between input and output can be efficiently extracted, and a comprehensive monitoring performance is provided. A numerical example and two industrial benchmarks are performed to demonstrate the efficiency of the proposed method.