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

Abstract

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.

A computer vision approach to improve maintenance automation for thermal power plants lubrication systems

“…The basic requirements of TPPs operations are to ensure safety, economy and power quality (Raza and Hameed, 2022). From an operational perspective, modern TPPs consist in very complex industrial integrated systems, characterized by extensive pipework (Bao et al, 2022) requiring continuous improvement in all aspects of their operations, including maintenance, in order to ensure competitive operational performance (Singh and Gurtu, 2022). Therefore, accurate and timely detection of anomalies is essential for the safe and economic operation of complex TPP environments (He et al, 2022).…”

“…As a maintenance method to find the cause of the failure from the fault phenomenon, CM plays a very effective role in the maintenance and improvement after the failure. As in TPPs, image recognition technology be used as a CM tool to monitor liquid leakage pipelines and provide timely corrections and optimizations (Bao et al. , 2022).…”