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A data-driven subspace distributed fault detection strategy for linear heterogeneous multi-agent systems.

Nasim Yazdanpanah1, Malihe Maghfoori Farsangi1, Saeid R Seydnejad1

  • 1Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

ISA Transactions
|January 24, 2024
PubMed
Summary
This summary is machine-generated.

A new data-driven fault detection strategy for linear heterogeneous multi-agent systems (MASs) was developed. This method effectively identifies simultaneous faults without needing agent communication, enhancing system reliability.

Keywords:
Data-Driven Fault DetectionHeterogeneous Multi-agent systemsSubspace Identification Method

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Multi-agent systems (MASs) often face challenges in fault detection due to heterogeneous agent dynamics.
  • Existing fault detection methods may require centralized control or inter-agent communication, limiting scalability and robustness.

Purpose of the Study:

  • To introduce a novel, data-driven, distributed fault detection strategy for linear heterogeneous MASs.
  • To address the limitations of existing methods by eliminating the need for centralized information or communication.

Main Methods:

  • Employed subspace construction techniques to model the normal behavior of individual agents.
  • Developed a decentralized approach that processes data locally on each agent.
  • Focused on detecting deviations from normal operational parameters.

Main Results:

  • Successfully demonstrated the ability to detect simultaneous faults in different agents within a heterogeneous MAS.
  • Validated the effectiveness and efficiency of the proposed data-driven strategy through simulations.
  • Showcased the independence of the fault detection process from inter-agent communication.

Conclusions:

  • The proposed data-driven subspace distributed fault detection strategy offers a significant advancement for heterogeneous MASs.
  • This approach provides a robust and efficient tool for enhancing the reliability and safety of complex multi-agent systems.
  • The method's decentralized nature makes it suitable for large-scale and complex systems where communication is constrained.