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Abnormal patterns recognition in bivariate autocorrelated process using optimized random forest and multi-feature

Yu-Wei Wan1, Bo Zhu1

  • 1School of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China.

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|October 11, 2020
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Summary
This summary is machine-generated.

This study introduces an optimized random forest model for industrial process control. The new method accurately identifies specific abnormal variables, improving real-time monitoring and control accuracy.

Keywords:
Bivariate autocorrelated processMulti-feature extractionPSOPattern recognitionRandom forest

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Area of Science:

  • Industrial Engineering
  • Machine Learning
  • Statistical Process Control

Background:

  • Traditional multivariate control charts struggle to pinpoint specific abnormal variables during process deviations.
  • Accurate identification of abnormal variables is crucial for effective real-time process control and quality management.

Purpose of the Study:

  • To develop an advanced model for identifying specific abnormal variables in industrial processes.
  • To enhance the accuracy and effectiveness of process abnormality detection using machine learning.

Main Methods:

  • Defined four patterns of process states based on combinations of abnormal variables.
  • Extracted statistical and shape features to create a feature vector for input into a random forest (RF) model.
  • Utilized particle swarm optimization (PSO) to optimize key parameters of the RF model.

Main Results:

  • The proposed model achieved a significant accuracy increase from 91.25% to 98.33% after multi-feature extraction and PSO optimization.
  • Simulation experiments demonstrated the superiority of the proposed model when compared to other algorithms.
  • The model effectively identifies specific abnormal variables, overcoming limitations of traditional methods.

Conclusions:

  • The enhanced random forest model with multi-feature extraction and PSO optimization shows high accuracy in identifying specific abnormal variables.
  • This approach offers a promising solution for real-time process control and abnormality detection in industrial settings.