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Production System Monitoring Based on Petri Nets Enhanced with Multi-Source Information.

Peng Liu1, Xinze Li1, Chenlong Zhang2

  • 1School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130012, China.

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Summary
This summary is machine-generated.

This study introduces wearable sensors and an enhanced Petri nets model for intelligent manufacturing quality control. This approach offers real-time operational insights, improving automated production monitoring and decision-making.

Keywords:
Petri netscondition monitoringproduction linesmart helmet

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Area of Science:

  • Industrial Engineering
  • Manufacturing Systems
  • Automation Technology

Background:

  • Current manufacturing quality control relies on fixed, costly equipment, lacking operator perspective.
  • Existing systems often prioritize visual data over integrated simulation and state detection, causing response delays.

Purpose of the Study:

  • To develop a flexible, operator-centric approach for enhanced industrial state perception.
  • To create a dynamic modeling and real-time visual representation system for production lines.

Main Methods:

  • Implementation of wearable sensing technology for on-site operators.
  • Development of a multi-source information-enhanced Petri nets model for dynamic process representation.

Main Results:

  • The proposed method provides an intuitive, dynamic, and accurate reflection of real-time production status.
  • Successful implementation in a reinforced concrete civil defense door production system.

Conclusions:

  • Wearable sensing and enhanced Petri nets improve industrial scenario perception and operational decision-making.
  • The approach offers a scalable solution applicable to diverse manufacturing environments.