A Novel Soft Sensing Method for Transient Processes Regression Utilizing Locally Weighted PLS

This paper develops a novel soft sensing method using locally weighted partial least squares (PLS) for transient processes regression. Industrial transient processes cannot be described using merely one model and therefore the regression model should be updated according to the online system condition. Different from previous just-in-time (JIT) methods using Euclidean distance, a supervised approach is proposed involving both process data X and quality data Y to finish sample selection tasks. The locally weighted PLS is adopted to depict the relation between X and Y. The performance of the novel soft sensing structure is validated by an industrial process.

[1]  Roman Rosipal,et al.  Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space , 2002, J. Mach. Learn. Res..

[2]  W. Massy.  Principal Components Regression in Exploratory Statistical Research , 1965 .

[3]  Biao Huang,et al.  Recursive Wavelength-Selection Strategy to Update Near-Infrared Spectroscopy Model with an Industrial Application , 2013 .

[4]  Biao Huang,et al.  A unified recursive just-in-time approach with industrial near infrared spectroscopy application , 2014 .

[5]  Richard Kramer.  Principal Component Regression , 1998 .

[6]  Manabu Kano,et al.  Covariance-based Locally Weighted Partial Least Squares for High- Performance Adaptive Modeling , 2015 .

[7]  In-Beum Lee,et al.  A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction , 2005 .

[8]  Jialin Liu,et al.  Development of Self-Validating Soft Sensors Using Fast Moving Window Partial Least Squares , 2010 .

[9]  Y. Yamashita,et al.  Co-learning with a locally weighted partial least squares for soft sensors of nonlinear processes , 2014 .

[10]  George Cybenko,et al.  Just-in-Time Learning and Estimation , 1996 .

[11]  Principal Component Regression , 2018 .

[12]  Yu Long,et al.  Kernel Partial Least-Squares Regression , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[13]  Min-Sen Chiu,et al.  Nonlinear process monitoring using JITL-PCA , 2005 .

[14]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[15]  Zhiqiang Ge,et al.  Double locally weighted principal component regression for soft sensor with sample selection under supervised latent structure , 2016 .

[16]  Qian Feng.  Soft Sensor Modeling Based on PCA and Support Vector Machines , 2006 .

[17]  Ying-wei Zhang,et al.  Complex process monitoring using modified partial least squares method of independent component regression , 2009 .

[18]  Wei Shen,et al.  A soft sensor modeling approach using support vector machines , 2003, Proceedings of the 2003 American Control Conference, 2003..

[19]  Shih-Ying Chang,et al.  Implementation of Locally Weighted Regression to Maintain Calibrations on FT-NIR Analyzers for Industrial Processes , 2001 .

[20]  Zhi-huan Song,et al.  Locally Weighted Kernel Principal Component Regression Model for Soft Sensing of Nonlinear Time-Variant Processes , 2014 .

[21]  Zhiqiang Ge,et al.  A comparative study of just-in-time-learning based methods for online soft sensor modeling , 2010 .

[22]  Richard D. Braatz,et al.  Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes , 2000 .

[23]  M. Chiu,et al.  A new data-based methodology for nonlinear process modeling , 2004 .

[24]  Manabu Kano,et al.  Development of soft-sensor using locally weighted PLS with adaptive similarity measure , 2013 .