Unmanned Aerial Vehicles and Artificial Intelligence Technologies as a Tool for Automating of Thermal Power Plant Boiler Monitoring

被引：0
|
作者：
Kalyagin, M. Yu. [1 ]
Vititin, V.F. [1 ]
Kondarattsev, V.L. [1 ]
机构：
[1] Moscow Aviation Institute, Moscow,125993, Russia
来源：
Russian Engineering Research | 2024年 / 44卷 / 08期
关键词：
Abstract: Approaches to creating an automated system for monitoring the inner surface of a thermal power plant boiler using small-sized unmanned aerial vehicles; neural networks; and computer vision technologies are considered. Analysis of visual defects in boiler tubes made it possible to identify five main types of defects; for each of them datasets are created using augmentation and synthetic data generation procedures. Three neural networks (YOLOv4; DetectoRS; and DCN) are trained using the datasets. Their characteristics are determined experimentally; and a comparative analysis of the reliability and speed of defect detection is carried out. © Allerton Press; Inc; 2024; ISSN; 1068-798X; Russian Engineering Research; Vol; 44; No; 8; pp. 1215–1219. Allerton Press; 2024. Russian Text The Author(s); published in STIN; 7; pp; 48–52;
D O I：
10.3103/S1068798X24701971
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：1215 / 1219
页数：4
相关论文
共 50 条
  • [31] Empirical Case Study on Applying Artificial Intelligence and Unmanned Aerial Vehicles for the Efficient Visual Inspection of Residential Buildings
    Shin, Hyunkyu
    Kim, Jonghoon
    Kim, Kyonghoon
    Lee, Sanghyo
    BUILDINGS, 2023, 13 (11)
  • [32] Automatic Detection of Corrosion in Large-Scale Industrial Buildings Based on Artificial Intelligence and Unmanned Aerial Vehicles
    Lemos, Rafael
    Cabral, Rafael
    Ribeiro, Diogo
    Santos, Ricardo
    Alves, Vinicius
    Dias, Andre
    APPLIED SCIENCES-BASEL, 2023, 13 (03):
  • [33] Condition monitoring system for solar power plants with radiometric and thermographic sensors embedded in unmanned aerial vehicles
    Garcia Marquez, Fausto Pedro
    Segovia Ramirez, Isaac
    MEASUREMENT, 2019, 139 : 152 - 162
  • [34] Simulation of the Power Transmission Lines Electrical Field to Ensure Safe Navigation of the Unmanned Aerial Vehicles at Their Monitoring
    Rezinkina, M. M.
    Rezinkin, O. L.
    Chrzan, K. L.
    Lytvynenko, S. A.
    Veselova, N. V.
    2019 MODERN ELECTRIC POWER SYSTEMS (MEPS), 2019,
  • [35] An artificial intelligence approach to real-time automatic smoke detection by unmanned aerial vehicles and forest observation systems
    Kinaneva, Diyana
    Hristov, Georgi
    Georgiev, Georgi
    Kyuchukov, Petko
    Zahariev, Plamen
    PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON BIOMEDICAL INNOVATIONS AND APPLICATIONS (BIA 2020), 2020, : 133 - +
  • [36] DESIGN ON THE INSTALLATION POSITION OF THERMAL STRESS MONITORING SYSTEM FOR CFBC BOILER OF THERMAL POWER PLANT
    Kim, Ha-Geun
    Shin, Sang-Beom
    Kim, Dae-Soon
    PROCEEDINGS OF THE ASME PRESSURE VESSELS AND PIPING CONFERENCE - 2013, VOL 3: DESIGN AND ANALYSIS, 2014,
  • [37] GIS-Based Information System for Automated Building Facade Assessment Based on Unmanned Aerial Vehicles and Artificial Intelligence
    Chen, Kaiwen
    Reichard, Georg
    Xu, Xin
    Akanmu, Abiola
    JOURNAL OF ARCHITECTURAL ENGINEERING, 2023, 29 (04)
  • [38] Exergic analysis of new boiler technologies within power-generating units of thermal power plant
    Nozdrenko, G. V.
    Shchinnikov, P. A.
    Serant, F. A.
    Tomilov, V. G.
    Zykova, N. G.
    Kovalenko, P. Yu.
    Russkikh, E. E.
    THERMOPHYSICS AND AEROMECHANICS, 2009, 16 (02) : 315 - 323
  • [39] Exergic analysis of new boiler technologies within power-generating units of thermal power plant
    G. V. Nozdrenko
    P. A. Shchinnikov
    F. A. Serant
    V. G. Tomilov
    N. G. Zykova
    P. Yu. Kovalenko
    E. E. Russkikh
    Thermophysics and Aeromechanics, 2009, 16 : 315 - 323
  • [40] The Use of Unmanned Aerial Vehicles to Determine Differences in Vegetation Cover: A Tool for Monitoring Coastal Wetland Restoration Schemes
    Dale, Jonathan
    Burnside, Niall G.
    Hill-Butler, Charley
    Berg, Maureen J.
    Strong, Conor J.
    Burgess, Heidi M.
    REMOTE SENSING, 2020, 12 (24) : 1 - 16
12345