Batch process monitoring by kernel similarity-based support vector data description

WANG Jianlin1 MA Linyu1 LIU Weimin1 QIU Kepeng1 YU Tao1

(1.College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China 100029)

【Abstract】Kernel distance-based support vector data description (SVDD) for batch process monitoring exhibited poor monitoring precision by setting control limit from the largest kernel distance in historical process dataset but ignoring hyperspherical irregularity in high dimensional space. A kernel similarity based SVDD monitoring method was proposed for batch process monitoring. Kernel similarity was taken as kernel function value between support vectors and data samples for testing. The weighted summation of kernel similarity and distance of support vectors at various time points was utilized to set dynamic control limit for data samples of batch process to be monitored. Batch process monitoring was achieved by judging if kernel distance of test sample exceeded the dynamic control limit. This monitoring method considered irregularity of hypersphere, local distribution characteristics of process dataset in high dimensional space, and spontaneity of data samples, so that it could improve accuracy in batch process monitoring. The method effectiveness was demonstrated by numerical simulation and metal etching process in semiconductor manufacturing.

【Keywords】 kernel similarity; support vector data description; dynamic monitoring; batch process;


【Funds】 National Natural Science Foundation of China (61240047) Natural Science Foundation of Beijing, China (4152041)

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This Article


CN: 11-1946/TQ

Vol 68, No. 09, Pages 3494-3500

September 2017


Article Outline


  • Introduction
  • 1 Process monitoring based on SVDD
  • 2 Batch process monitoring based on kernel similarity SVDD
  • 3 Experimental verification
  • 4 Conclusion
  • References