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Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application

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  • Kaixiang Peng
  • Kai Zhang
  • Gang Li

Abstract

Projection to latent structures (PLS) model has been widely used in quality-related process monitoring, as it can establish a mapping relationship between process variables and quality index variables. To enhance the adaptivity of PLS, kernel PLS (KPLS) as an advanced version has been proposed for nonlinear processes. In this paper, we discuss a new total kernel PLS (T-KPLS) for nonlinear quality-related process monitoring. The new model divides the input spaces into four parts instead of two parts in KPLS, where an individual subspace is responsible in predicting quality output, and two parts are utilized for monitoring the quality-related variations. In addition, fault detection policy is developed based on the T-KPLS model, which is more well suited for nonlinear quality-related process monitoring. In the case study, a nonlinear numerical case, the typical Tennessee Eastman Process (TEP) and a real industrial hot strip mill process (HSMP) are employed to access the utility of the present scheme.

Suggested Citation

  • Kaixiang Peng & Kai Zhang & Gang Li, 2013. "Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-14, December.
    • Handle: RePEc:hin:jnlmpe:707953
    DOI: 10.1155/2013/707953
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    Cited by:

    1. Zhou, Chengyu & Fang, Xiaolei, 2023. "A convex two-dimensional variable selection method for the root-cause diagnostics of product defects," Reliability Engineering and System Safety, Elsevier, vol. 229(C).

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