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Improved correlation analysis and visualization of industrial alarm data.

F Yang1, S L Shah, D Xiao

  • 1Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 2V4, Canada. yangfan@tsinghua.edu.cn

ISA Transactions
|April 17, 2012
PubMed
Summary

This study introduces a Gaussian kernel method for analyzing complex industrial alarms. The new approach effectively identifies correlated alarms, improving smart alarm management and reducing false alerts.

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Area of Science:

  • Industrial engineering
  • Data science
  • Control systems

Background:

  • Multivariate alarm analysis is crucial for smart alarm management due to complex variable interrelationships.
  • Visualizing historical alarm data correlations aids in understanding system behavior.
  • Existing methods may not adequately handle noise like missed or false alarms.

Purpose of the Study:

  • To develop a robust method for analyzing and rationalizing multivariate alarms using historical data.
  • To enhance the identification of correlated alarms and redundancies for improved alarm settings.
  • To provide a practical and effective tool for industrial alarm management.

Main Methods:

  • Applied Gaussian kernel method to generate pseudo-continuous time series from binary alarm data.
  • Utilized correlation color maps with reordered alarm tags to visualize variable clusters.
  • Incorporated time lags and singular value decomposition for in-depth analysis within clusters.

Main Results:

  • The Gaussian kernel method effectively reduces the impact of noisy alarm data (missed, false, chattering).
  • Correlation color maps clearly display clusters of related alarms, facilitating analysis.
  • The proposed method demonstrated superior performance compared to alarm similarity color maps in industrial case studies.

Conclusions:

  • The Gaussian kernel method offers a practical and effective approach to multivariate alarm analysis.
  • This technique improves the identification of alarm correlations and redundancies, leading to optimized alarm settings.
  • The method provides a valuable tool for enhancing smart alarm management in industrial settings.