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Linear Temporal Logic (LTL) Based Monitoring of Smart Manufacturing Systems.

Gerald Heddy1, Umer Huzaifa1, Peter Beling1

  • 1University of Virginia, Charlottesville, VA, 22904, USA.

Proceedings of the Annual Conference of the Prognostics and Health Management Society. Prognostics and Health Management Society. Conference
|July 22, 2017
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Summary
This summary is machine-generated.

This study introduces a novel robotic monitoring system for Smart Manufacturing Systems (SMS). It addresses challenges in Prognostics and Health Management (PHM) by integrating supervisory control and model checking for enhanced robot reliability.

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Area of Science:

  • Robotics
  • Manufacturing Systems
  • Prognostics and Health Management (PHM)

Background:

  • Smart Manufacturing Systems (SMS) require adaptable collaborative robots.
  • Managing environmental variables with discrete logic programming presents challenges.
  • Effective performance and health monitoring of robotic systems are crucial.

Purpose of the Study:

  • To develop a robotic monitoring system to address the disconnect between discrete controller faults and continuous physical component degradation.
  • To present a novel approach for Prognostics and Health Management (PHM) in robotic systems.
  • To demonstrate the methodology in an industry-inspired use-case.

Main Methods:

  • Leveraging supervisory robotic control.
  • Utilizing model checking with linear temporal logic (LTL).
  • Developing a monitoring system for PHM.

Main Results:

  • A novel monitoring system for PHM was presented.
  • The methodology was demonstrated in a MATLAB-based simulator for an industry-inspired use-case.
  • The system effectively captures and resolves the disconnect between logical and physical component health.

Conclusions:

  • The proposed PHM approach using supervisory control and LTL model checking is effective for robotic systems.
  • This methodology provides a foundation for future adaptive control strategies.
  • The system enhances the reliability and longevity of robotic systems in smart manufacturing.