A fault detection algorithm for pipeline insulation layer based on immune neural network
Title
A fault detection algorithm for pipeline insulation layer based on immune neural network
Subject
Fault detection
Immune neural network
Infrared image
Pipeline insulation layer
Description
Images of the pipeline can be easily captured by the infrared thermal camera. However, factors such as uneven lighting or the complex and diverse outdoor environment can affect the pipeline infrared image. Under these circumstances, detection of the abnormally high-temperature areas of pipe insulation becomes very difficult because the contrast between the image target and background is reduced. This work aims to overcome these issues and presents an immune neural network algorithm. The existing artificial immune algorithm only reflects the function and characteristics of adaptive immunity, without considering the holistic nature of the biological immune system. This method draws on the synergy mechanism of the innate immune network and the adaptive immune network in the biological immune system and combines the holistic immune system with the neural network. The method can effectively detect abnormally high-temperature areas in the pipeline infrared images caused by insulation faults and has higher detection accuracy compared with other image detection algorithms.
104611
196
Creator
Yu, Xiao
Tian, Xuesong
Publisher
International Journal of Pressure Vessels and Piping
Date
2022
Type
journalArticle
Identifier
0308-0161
10.1016/j.ijpvp.2022.104611
Collection
Citation
Yu, Xiao and Tian, Xuesong, “A fault detection algorithm for pipeline insulation layer based on immune neural network,” Lamar University Midstream Center Research, accessed May 13, 2024, https://lumc.omeka.net/items/show/27560.