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Stream-data-clustering based adaptive alarm threshold setting approaches for industrial processes with multiple

Yuehan Wang1, Jince Li1, Bo Yang1

  • 1College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China.

ISA Transactions
|February 15, 2022
PubMed
Summary

This study introduces an adaptive alarm threshold method using stream data clustering (SDC) to improve industrial alarm management. The approach effectively handles changing operating conditions for more reliable process monitoring.

Keywords:
Adaptive alarm threshold settingIndustrial processMulti-conditions analysisStreaming data clustering

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Area of Science:

  • Industrial Process Control
  • Data Science
  • Chemical Engineering

Background:

  • Effective alarm management is crucial for industrial processes.
  • Conventional alarm thresholds fail to adapt to changing operating conditions, reducing system effectiveness.
  • Dynamic industrial environments necessitate adaptive alarm threshold strategies.

Purpose of the Study:

  • To propose an adaptive alarm threshold setting approach for industrial processes.
  • To enhance the reliability and effectiveness of alarm management systems.
  • To address the limitations of static alarm thresholds in dynamic production environments.

Main Methods:

  • Developed a stream data clustering algorithm (a-DenStream) for online micro-clustering and offline integration of industrial flow data.
  • Utilized the C-BOUND algorithm to extract cluster edges for defining operational states.
  • Implemented segmentation and multi-condition alarm threshold modeling for adaptive threshold setting based on model matching.

Main Results:

  • The proposed adaptive method demonstrated effectiveness in experiments on a coal gasification chemical process.
  • The approach successfully sets alarm thresholds for multiple operating conditions.
  • Improved accuracy and reliability of alarm management systems under varying process dynamics.

Conclusions:

  • The adaptive alarm threshold setting method based on stream data clustering offers a significant improvement over conventional approaches.
  • This method provides a viable solution for alarm management in industrial processes with multiple operating conditions.
  • The study highlights the potential for enhanced process safety and efficiency through adaptive alarm systems.