A General Quality-related Nonlinear Process Monitoring Approach Based on Input-Output Kernel PLS

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...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2023-01, Vol.72, p.1-1
Main Authors: Kong, Xiangyu, Luo, Jiayu, Feng, Xiaowei, Liu, Meizhi
Format: Article
Language:English
Subjects:
Data mining
Data-driven
fault detection
Feature extraction
Kernel
kernel projection to latent structure (KPLS)
Kernels
Latent class analysis
Linear programming
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Monitoring
nonlinear process monitoring
Nonlinearity
Principal component analysis
Process monitoring
quality-related
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Summary: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 kernel PLS (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.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2023.3238692