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APPLICATION MODELLING OF INTELLIGENT DIAGNOSIS FOR THERMAL CYCLE SYSTEM

ABSTRACT
In order to solve the problem of intelligent fault diagnosis of thermal system of thermal power unit, the application modelling of intelligent diagnosis of thermal cycle system is proposed. This paper first filters the influence of the change of valve opening of steam turbine on the performance index under the condition of constant power, and qualitatively analyzes the actual change of the performance index compared with the reference working condition when the component fails, so as to eliminate the mutual influence between the components. Then, the selection principle of thermodynamic parameters is determined, the irreversible loss in the structural theory of thermal economics is introduced as the performance index, and the model is used to quantitatively calculate the change of performance index of each component under fault conditions to diagnose the failed component. Finally, APROS simulation software is used to simulate various fault conditions of 330 MW units in a power plant. The experimental results show that the recognition accuracy of the monitoring system designed in this paper can reach 98.33%. In conclusion, the method in this paper proves the feasibility of intelligent fault diag­nosis of thermodynamic system network.
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PAPER SUBMITTED: 2022-07-18
PAPER REVISED: 2022-09-07
PAPER ACCEPTED: 2022-09-22
PUBLISHED ONLINE: 2023-03-25
DOI REFERENCE: https://doi.org/10.2298/TSCI2302101L
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THERMAL SCIENCE YEAR 2023, VOLUME 27, ISSUE Issue 2, PAGES [1101 - 1108]
REFERENCES
  1. Jung, Y., Cho, M. K., Impacts of Reporting Lines and Joint Reviews on Internal Audit Effectiveness, Managerial Auditing Journal, 37 (2022), 4, pp. 486-518
  2. Wang, R., et al., Cultivating Consumer Subjective Well-Being through Online Brand Communities: A Multidimensional View of Social Capital, Journal of Product and Brand Management, 31 (2022), 5, pp. 808-822
  3. Wang, Q., Wei, S., Development and Application of Methane Leakage Monitoring System for Gas Transmission Pipe-Line, Electronic Research and Applications, 5 (2021), 6, pp. 44-49
  4. Zhao, Y., et al., Modelling and Dynamics Simulation of Spur Gear System Incorporating the Effect of Lubrication Condition and Input Shaft Crack, Engineering Computations, 39 (2022), 5, pp. 1669-1700
  5. Miri, S., et al., Tensile and Thermal Properties of Low-melt Poly Aryl Ether Ketone Reinforced with Continuous Carbon Fiber Manufactured by Robotic 3-D Printing, The International Journal of Advanced Manufacturing Technology, 122 (2022), 2, pp. 1041-1053
  6. Hegde, N., et al., A Survey on Machine Learning and Deep Learning-Based Computer-Aided Methods for Detection of Polyps in CT Colonography, Current Medical Imaging Reviews, 17 (2021), 1, pp. 3-15
  7. Parham, Kebria, M., et al., Deep Imitation Learning for Autonomous Vehicles Based on Convolutional Neural Networks, IEEE/CAA Journal of Automatica Sinica, 7 (2020), 01, pp. 85-98
  8. Ghosh, M. K., Pradhan, S., A Non-Zero-Sum Risk-Sensitive Stochastic Differential Game in the Orthant, Mathematical Control, Related Fields, 12 (2022), 2, pp. 343-370
  9. Alkheder, S., et al., A Socio-Economic Study for Establishing an Environment-Friendly Metro in Kuwait, International Journal of Social Economics, 49 (2022), 5, pp. 685-709
  10. Cai, W., Hu, D., The Qrs Complex Detection Using Novel Deep Learning Neural Networks, IEEE Access, 8 (2020), May, pp. 97082-97089

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence