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A Novel Data-Driven Fault Detection Method Based on Stable Kernel Representation for Dynamic Systems.

Qiang Wang1, Bo Peng2, Pu Xie3

  • 1Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.

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Summary
This summary is machine-generated.

This study introduces a novel data-driven fault detection (FD) method for large-scale dynamic systems. The approach enhances FD sensitivity using Hellinger distance and subspace techniques for improved industrial monitoring.

Keywords:
Hellinger distancedata-driven designsdistributed frameworkfault detection (FD)sensor networkssubspace identification

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Area of Science:

  • Industrial automation and control systems engineering.
  • Data-driven fault detection methodologies.
  • Advanced signal processing for dynamic systems.

Background:

  • Modern industrial systems are increasingly large-scale due to advancements in manufacturing and big data.
  • Centralized fault detection (FD) frameworks face limitations in dynamic systems, particularly regarding sensitivity.
  • Need for distributed and sensitive FD methods using only system input/output data.

Purpose of the Study:

  • To propose a novel data-driven fault detection (FD) method for large-scale dynamic systems.
  • To enhance the sensitivity and overcome drawbacks of centralized FD in dynamic environments.
  • To utilize system input/output data from sensor networks for distributed residual signal generation.

Main Methods:

  • A data-driven fault detection (FD) approach combining Hellinger distance and subspace techniques.
  • Generation of distributed residual signals via stable kernel representation of the process.
  • Utilization of average consensus algorithms for identical residual signal and test statistic at each sensor node.
  • Integration of Hellinger distance for improved residual signal analysis and FD performance.

Main Results:

  • The proposed method generates identical residual signals and test statistics across sensor nodes.
  • Hellinger distance integration demonstrably improves fault detection performance.
  • Effectiveness and accuracy validated in a real-world multiphase flow facility.

Conclusions:

  • The developed data-driven method offers a sensitive and accurate approach to fault detection in large-scale dynamic systems.
  • Distributed residual signal generation via consensus algorithms enhances robustness.
  • The integration of Hellinger distance provides a significant performance improvement for fault detection.