THE USE OF A NEURAL NETWORK FOR IDENTIFICATION OF BURNOUT ZONES IN DIAGNOSTICS OF THE LINING OF CRITICAL LINED EQUIPMENT
https://doi.org/10.26896/1028-6861-2018-84-4-27-33
Abstract
A method of automated diagnostics of critical lined equipment is improved using a neural network to recognize the thermograms and classify the burnout zones. The proposed method provides an increase in the reliability and promptness in determination of the lining burnout zones and their qualitative assessment. The information signs used for analysis and recognition of the lining burnout zones on a thermogram image are considered. The results of using neural networks with different configurations to minimize the error of classifying the levels of the burnout and determining the optimal number of the learning epochs are presented. The developed automated system for technical diagnostics of critical lined equipment, including the software for analysis and recognition of the thermograms, was evaluated and implemented at the enterprises of metallurgical production. Comparative analysis of the results obtained using the developed automated system and traditional system of diagnostics demonstrated the advantages of the developed method.
About the Authors
V. A. Yemelyanov
Financial University under the Government of the Russian Federation
Russian Federation
Russian Federation
Vitaliy A. Yemelyanov
Moscow
N. Yu. Yemelyanova
Financial University under the Government of the Russian Federation
Russian Federation
Russian Federation
Nataliya Yu. Yemelyanova
Moscow
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Review
For citations:
Yemelyanov V.A., Yemelyanova N.Yu. THE USE OF A NEURAL NETWORK FOR IDENTIFICATION OF BURNOUT ZONES IN DIAGNOSTICS OF THE LINING OF CRITICAL LINED EQUIPMENT. Industrial laboratory. Diagnostics of materials. 2018;84(4):27-33. (In Russ.) https://doi.org/10.26896/1028-6861-2018-84-4-27-33
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