A Machine Vision-Based Pipe Leakage Detection System for Automated Power Plant Maintenance

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-02, Vol.22 (4), p.1588
• Main Authors: Bao, Nengsheng, Fan, Yuchen, Ye, Zihao, Simeone, Alessandro
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Automation; Classification; Comparative analysis; Contours; Decision making; Detectors; Electric power-plants; High definition; Image acquisition; Image classification; Image processing; Image Processing, Computer-Assisted - methods; Industrial plant emissions; Infrared imagery; Inspection; Leak detection; Light sources; Localization; Machine learning; Machine vision; maintenance; Mechanization; Neural networks; Optical Devices; Pipework; Plant maintenance; Power Plants; Quality control equipment; Sensors; Support vector machines; system monitoring; Technology application; Visibility; vision system; Vision systems
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22041588

Industrial pipework maintenance inspection can be automated through machine vision-based effusion monitoring. However, colorless effusions such as water can be difficult to detect in a complex industrial environment due to weak illumination and poor visibility of the background. This paper deploys the reflective characteristics of effusion and its lower temperature compared to the environment in order to develop an automatic inspection system for power plant pipeworks' maintenance. Such a system is aimed at detecting the colorless fluid effusion based on dual source images and a contour features algorithm. In this respect, a visible light source unit highlights the reflective features of the effusion edge. Meanwhile, high-definition images of the potential effusion are acquired under both visible and infrared lights. A customized image processing procedure extracts the potential effusion features from the infrared image to retrieve the region of interest for segmentation purposes and transfer such information to the visible light image to determine the effusion contour. Finally, a decision-making support tool based on the image contour closure is enabled for classification purposes. The implementation of the proposed system is tested on a real industrial environment. Experimental results show a classification accuracy up to 99%, demonstrating excellent suitability in meeting industrial requirements.