Author:
G. Biradar Vidyadevi,Nagaraj H.C.,Mohan S.G.,Kumar Pareek Piyush
Abstract
The fatigue state of fluid components such as valves, metal surfaces in gas or oil carrying pipelines is important to monitor on regular basis and plan for repair work to avoid risks associated with them, this becomes more crucial when the pipelines are supplying hazard prone fluids. There exist methods for detection of corroded surfaces, scratches and fractures in pipelines, valves, and regulators etcetera. The conventional methods are based on sensors and chemical analysis methods. There are challenges with conventional methods pertaining to the desired metric of scalability and disadvantages of these methods is they are contact based and destructive methods. Therefore, to overcome these limitations of existing methods there is a need for development of non-contact and nondestructive methods. The recent advancements in Artificial Intelligence technology in every domain including health care monitoring, agriculture sector, defense applications and civilian applications etc., have shown that deep learning methods can be explored in industrial applications to develop fault tolerant systems which help fluid components state of health monitoring through computer vision. In this chapter proposes various methods for analysis of health state of fluid components using deep convolutional neural networks and suggest the best models for these applications.
Cited by
1 articles.
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