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A missing variable approach for decentralized statistical process monitoring.

Chudong Tong1, Ting Lan2, Ying Zhu2

  • 1Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo, 315211, PR China; Key Laboratory of Advanced Control and Optimization for Chemical Processes (East China University of Science and Technology), Ministry of Education, PR China.

ISA Transactions
|September 29, 2018
PubMed
Summary
This summary is machine-generated.

A novel missing variable approach enhances decentralized process monitoring using principal component analysis (PCA). This method improves monitoring accuracy by analyzing residuals, even without Gaussian data assumptions.

Keywords:
Decentralized monitoringMissing variablePrincipal component analysisResidual generation

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Area of Science:

  • Chemical Engineering
  • Data Science
  • Process Control

Background:

  • Principal Component Analysis (PCA) is widely used for process monitoring.
  • Traditional PCA struggles with missing data and requires Gaussian distributions.
  • Decentralized monitoring is crucial for complex industrial processes.

Purpose of the Study:

  • To investigate and apply a missing variable approach for decentralized process monitoring.
  • To develop a PCA model capable of handling missing data and non-Gaussian distributions.
  • To enhance the accuracy and robustness of process monitoring systems.

Main Methods:

  • Implemented a missing variable approach within a PCA framework.
  • Generated score and residual estimation errors by sequentially assuming one missing variable.
  • Developed an offline modeling and online monitoring strategy for decentralized application.
  • Monitored residuals instead of original data for improved sensitivity.

Main Results:

  • The missing variable approach effectively computes score and residual estimation errors.
  • Generated residuals tend towards a Gaussian distribution, relaxing data normality constraints.
  • The developed model demonstrated superior monitoring performance compared to existing methods.
  • Effectiveness validated through two industrial case studies.

Conclusions:

  • The missing variable approach offers a robust and effective solution for decentralized process monitoring.
  • This method overcomes limitations of traditional PCA, particularly regarding data distribution.
  • The approach provides a significant advancement in industrial process monitoring capabilities.