Paper Title:
Research on Nonlinear Process Monitoring and Fault Diagnosis Based on Kernel Principal Component Analysis
  Abstract

In order to monitor nonlinear production process effectively, multivariate statistical process control based on kernel principal component analysis is applied to process monitoring and diagnosis. Squared prediction error (SPE) statistic of the kernel principal component analysis (KPCA) model is used for process monitoring, and the fault causes of the production process could be tracked by the methods of data reconstruction and the optimal neighbor selection strategy. Simulation data and Tennessee Eastman process data are used for model validation, as a result the proposed method has better performance on abnormality detecting, compared with multivariate statistical process control based on linear principal component analysis. What is more, the causes of the faults are tracked effectively, thus the production process can be adjusted to prevent substandard products.

  Info
Periodical
Key Engineering Materials (Volumes 413-414)
Edited by
F. Chu, H. Ouyang, V. Silberschmidt, L. Garibaldi, C.Surace, W.M. Ostachowicz and D. Jiang
Pages
583-590
DOI
10.4028/www.scientific.net/KEM.413-414.583
Citation
F. He, M. Li, J. H. Yang, J. W. Xu, "Research on Nonlinear Process Monitoring and Fault Diagnosis Based on Kernel Principal Component Analysis", Key Engineering Materials, Vols. 413-414, pp. 583-590, 2009
Online since
June 2009
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Price
$35.00
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