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Application-Aware Anomaly Detection of Sensor Measurements in Cyber-Physical Systems.

Sensors (Basel, Switzerland)·2018
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Fault-Adaptive Autonomy in Systems with Learning-Enabled Components.

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This summary is machine-generated.

This study introduces a new system for autonomous cyber-physical systems (CPS) to manage failures. The novel architecture enhances safety and mission success by enabling real-time fault detection, isolation, and reconfiguration.

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Area of Science:

  • Cyber-Physical Systems (CPS)
  • Autonomous Underwater Vehicles (AUV)
  • Machine Learning in Robotics

Background:

  • Autonomous CPS require robust failure management for safety and mission success.
  • Physical degradations in vehicles can cause unpredictable behavior, risking system integrity.
  • Existing systems may struggle with scalability and false positives in fault detection.

Purpose of the Study:

  • To present a novel Behavior Tree-based autonomy architecture for robust failure management in CPS.
  • To integrate a Fault Detection and Isolation Learning-Enabled Component (FDI LEC) with an Assurance Monitor (AM) using Inductive Conformal Prediction (ICP).
  • To enable real-time contingency management through fault detection, isolation, and reconfiguration.

Main Methods:

  • Developed a Behavior Tree-based autonomy architecture incorporating an FDI LEC and an AM based on ICP.
  • Implemented real-time contingency management with fault detection, isolation, and reconfiguration subsystems.
  • Designed decision-making logic with adjustable thresholds for fault coverage and risk to enhance scalability and reduce false positives.

Main Results:

  • The proposed architecture effectively manages failures in autonomous systems.
  • The integrated FDI LEC and AM demonstrated reliable fault detection and isolation.
  • Adjustable thresholds improved the scalability and reduced the false-positive rate of the FDI LEC.

Conclusions:

  • The novel architecture provides a robust solution for autonomous CPS failure management.
  • The FDI LEC with ICP-based AM enhances system safety and reliability.
  • The system's effectiveness was validated using a simulated autonomous underwater vehicle (AUV).