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

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.