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Generic Sensor Failure Modeling for Cooperative Systems.

Georg Jäger1, Sebastian Zug2, António Casimiro3

  • 1Otto-von-Guericke Universität Magdeburg, Embedded Smart Systems, 39106 Magdeburg, Germany. georg.jaeger@ovgu.de.

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|March 21, 2018
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Summary
This summary is machine-generated.

This study introduces a new generic failure model for cooperative systems, enhancing safety and maintainability. The model, extracted from sensor data, accurately captures system failures compared to traditional methods.

Keywords:
cooperative systemscyber-physical-systemsdynamically composed systemsgeneric failure modelingmaintaining safetysensor failures

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Area of Science:

  • Computer Science
  • Systems Engineering
  • Robotics

Background:

  • Cooperative systems feature dynamic component composition, unlike static systems.
  • Maintaining safety in dynamic systems requires novel approaches.
  • Current failure models do not meet the demands of cooperative systems.

Purpose of the Study:

  • To propose an integration step for evaluating shared information failure models.
  • To ensure the maintainability of cooperative system safety.
  • To develop a generic failure model and an automated extraction process.

Main Methods:

  • An integration step evaluating failure models against application fault tolerance.
  • Development of a mathematically defined generic failure model.
  • A processing chain for automatic failure model extraction from empirical data.
  • Analysis of data from a Sharp GP2D12 distance sensor.

Main Results:

  • The proposed generic failure model meets new requirements for cooperative systems.
  • The automated extraction process successfully derives failure models from sensor data.
  • The generic model accurately models failure characteristics of the Sharp GP2D12 sensor.
  • The new approach demonstrates superior failure modeling compared to traditional techniques.

Conclusions:

  • The developed generic failure model is suitable for cooperative systems.
  • Automated failure model extraction from empirical data is feasible and effective.
  • This work advances the safety and maintainability of dynamic cooperative systems.