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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 757828, 18 pages
http://dx.doi.org/10.1155/2012/757828
Research Article

Fault Detection for Industrial Processes

State Laboratory of Synthesis Automation of Process Industry, Northeastern University, Liaoning, Shenyang 110004, China

Received 23 August 2012; Accepted 15 November 2012

Academic Editor: Huaguang Zhang

Copyright © 2012 Yingwei Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A new fault-relevant KPCA algorithm is proposed. Then the fault detection approach is proposed based on the fault-relevant KPCA algorithm. The proposed method further decomposes both the KPCA principal space and residual space into two subspaces. Compared with traditional statistical techniques, the fault subspace is separated based on the fault-relevant influence. This method can find fault-relevant principal directions and principal components of systematic subspace and residual subspace for process monitoring. The proposed monitoring approach is applied to Tennessee Eastman process and penicillin fermentation process. The simulation results show the effectiveness of the proposed method.