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A correlation consistency based multivariate alarm thresholds optimization approach.

Huihui Gao1, Feifei Liu1, Qunxiong Zhu1

  • 1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China.

ISA Transactions
|September 26, 2016
PubMed
Summary
This summary is machine-generated.

Optimizing alarm thresholds improves process monitoring. This study proposes a new method to ensure correlation consistency between process and alarm data for better industrial control.

Keywords:
Alarm thresholds optimizationCorrelation analysisInterpretative structural modelingKernel density estimation

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Area of Science:

  • Chemical Engineering
  • Process Control
  • Industrial Automation

Background:

  • Alarm systems generate data influenced by threshold settings.
  • Inconsistent correlations between process and alarm data can lead to misinterpretation.
  • Optimizing alarm thresholds is crucial for effective process monitoring.

Purpose of the Study:

  • To propose a novel methodology for multivariate alarm threshold optimization.
  • To ensure correlation consistency between process and alarm data.
  • To improve the reliability of industrial alarm systems.

Main Methods:

  • Interpretative structural modeling for key variable selection.
  • Pearson correlation analysis for process data.
  • Kernel density estimation for alarm data correlation.
  • Particle swarm optimization for threshold determination.

Main Results:

  • A method to ensure correlation consistency between process and alarm data was developed.
  • Optimal alarm thresholds were determined based on correlation consistency.
  • The Tennessee Eastman process case study validated the method's effectiveness.

Conclusions:

  • The proposed methodology effectively optimizes multivariate alarm thresholds.
  • Correlation consistency is a key factor for reliable alarm data analysis.
  • This approach enhances the performance of industrial process monitoring systems.