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Batch process fault detection and identification based on discriminant global preserving kernel slow feature

Hanyuan Zhang1, Xuemin Tian2, Xiaogang Deng2

  • 1School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, Shandong, China.

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|May 20, 2018
PubMed
Summary
This summary is machine-generated.

Discriminant Global Preserving Kernel Slow Feature Analysis (DGKSFA) enhances batch process monitoring by integrating discriminant analysis with GKSFA. This novel method effectively detects faults and identifies fault variables in nonlinear dynamic systems.

Keywords:
Batch processDiscriminant analysisNonlinear biplotNonlinear fault variable identificationProcess monitoringSlow feature analysis

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Area of Science:

  • Chemical Engineering
  • Process Systems Engineering
  • Data Science

Background:

  • Global Preserving Kernel Slow Feature Analysis (GKSFA) is effective for nonlinear dynamic data analysis in batch processes.
  • Unsupervised GKSFA lacks the ability to use class label information, limiting its effectiveness in batch process monitoring.
  • Existing methods may not optimally handle the complexities of nonlinear time-varying dynamics in industrial processes.

Purpose of the Study:

  • To propose a novel Discriminant Global Preserving Kernel Slow Feature Analysis (DGKSFA) method for enhanced batch process monitoring.
  • To integrate discriminant analysis with GKSFA to leverage class label information for improved feature extraction.
  • To develop a nonlinear contribution plot for effective fault variable identification.

Main Methods:

  • Developed DGKSFA by combining discriminant analysis principles with GKSFA to extract discriminative features.
  • Preserved global and local geometrical structure information of observed data within the DGKSFA framework.
  • Constructed a monitoring statistic based on kernel feature vector distances for fault detection and utilized nonlinear contribution plots for fault diagnosis.

Main Results:

  • The proposed DGKSFA method successfully extracts discriminant features while preserving data geometry.
  • A novel monitoring statistic effectively detects faults in nonlinear dynamic systems.
  • The developed nonlinear contribution plot accurately identifies fault variables, distinguishing them from normal variables.

Conclusions:

  • DGKSFA offers a superior approach to batch process monitoring compared to traditional unsupervised methods.
  • The integrated method demonstrates robust performance in fault detection and diagnosis for nonlinear dynamic processes.
  • The approach was validated on a numerical nonlinear dynamic system and a fed-batch penicillin fermentation process.