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A Real-Time Intelligent Valve Monitoring Approach through Cameras Based on Computer Vision Methods.

Zihui Zhang1, Qiyuan Zhou1, Heping Jin2

  • 1School of Chemical Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
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This study introduces a computer vision system for real-time valve monitoring in industrial settings. The novel approach accurately detects abnormal valve positions, enhancing process safety and preventing accidents.

Area of Science:

  • Industrial Process Monitoring
  • Computer Vision Applications
  • Automation and Control Systems

Background:

  • Abnormal valve positions pose significant risks in the process industry, especially in frequently switched operations like green chemistry and fermentation.
  • Manual valve inspection is time-consuming and prone to errors, necessitating automated solutions.
  • The proliferation of cameras in industrial plants enables the application of computer vision for real-time monitoring.

Purpose of the Study:

  • To develop a novel, real-time computer vision-based approach for detecting abnormal valve positions.
  • To enhance the accuracy of valve position monitoring, particularly for small and fixed-position valves.
  • To improve industrial safety by providing timely alerts for critical valve status deviations.

Main Methods:

Keywords:
computer visioncoord attentionfeature pyramid networkloss preventionregional convolutional neural networkvalve monitoring

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  • Utilized an improved YOLO V8 network architecture for valve detection and feature recognition.
  • Integrated a coord attention module to embed position information and improve rotation feature extraction for small valves.
  • Employed a rotation algorithm using center points and bounding box coordinates to calculate valve position and trigger alarms.

Main Results:

  • The proposed method achieved high accuracy and robustness in detecting abnormal valve positions across three valve types and two industrial scenarios.
  • The coord attention module significantly improved the extraction of valve rotation features.
  • The system successfully met the stringent accuracy and reliability standards for real-time industrial monitoring.

Conclusions:

  • The developed computer vision approach offers a reliable and accurate solution for real-time valve monitoring in industrial processes.
  • This technology can significantly enhance operational safety and efficiency by automating valve status checks.
  • The method demonstrates strong generalization capabilities, applicable to various industrial settings and valve types.