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Global-and-local-structure-based neural network for fault detection.

Haitao Zhao1, Zhihui Lai2, Yudong Chen3

  • 1Automation Department, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 23, 2019
PubMed
Summary

A new fault detection method, the global-and-local-structure-based neural network (GLSNN), effectively monitors industrial processes. GLSNN reduces missed detection and false alarm rates for improved process safety and efficiency.

Keywords:
Dimension reductionFault detectionFeedforward neural networkPrincipal component analysisStatistical process monitoring

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Area of Science:

  • Process Monitoring and Control
  • Artificial Intelligence in Engineering
  • Statistical Process Control

Background:

  • Traditional fault detection methods struggle with complex, high-dimensional industrial data.
  • Maintaining both global and local data structures is crucial for accurate process monitoring.
  • Nonlinear data-driven approaches offer potential for enhanced fault detection.

Purpose of the Study:

  • To introduce a novel statistical fault detection method, the global-and-local-structure-based neural network (GLSNN).
  • To develop a nonlinear, data-driven technique that preserves both global and local process data structures.
  • To improve the accuracy and reliability of fault detection in industrial processes.

Main Methods:

  • Adaptive neural network training considering global variance and local geometrical structure.
  • Nonlinear feature extraction from high-dimensional process data to meaningful low-dimensional representations.
  • Application of Hotelling T² and Squared Prediction Error (SPE) statistics for online fault detection.

Main Results:

  • The proposed GLSNN method demonstrated superior performance on the Tennessee Eastman (TE) benchmark process.
  • GLSNN achieved significant reductions in missed detection rate (MDR) and false alarm rate (FAR).
  • Theoretical analysis and case studies validated the effectiveness of the GLSNN approach.

Conclusions:

  • GLSNN is a robust and effective method for nonlinear data-driven process monitoring and fault detection.
  • The method's ability to preserve global and local structures enhances detection accuracy.
  • GLSNN offers a promising advancement for industrial process safety and operational efficiency.