Abstract
In this paper, we describe the relevance of diagnosing the lining condition of steel ladles in metallurgical facilities. Accidents with steel ladles lead to losses and different types of damage in iron and steel works. We developed an algorithm for recognizing thermograms of steel ladles to identify burnout zones in the lining based on the technology and design of neural networks. A diagnostic system structure for automated evaluating of the technical conditions of steel ladles without taking them out of service has been developed and described.
Funder
The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)
Reference30 articles.
1. Numerical simulation of temperature field and thermal stress field in the new type of ladle with the nanometer adiabatic material
2. Large Data and AI Analysis Based Online Diagnosis System Application of Steel Ladle Slewing Bearing;Hu;Adv. Asset Manag. Cond. Monit. COMADEM Smart Innov. Syst. Technol.,2019
3. Mathematical modelling of thermal stratification phenomena in steel ladles;Putan;Int. J. Eng.,2009
4. Modelling of Temperature Distribution in Refractory Ladle Lining for Steelmaking