Keywords
leak detector
entropy
copula-entropy взаємна кореляційна функція
течешукач
ентропія
копула-ентропія
How to Cite
Abstract
A development and search for new approaches to determining the correlation between data on the technical condition of energy sector facilities in Ukraine is due to the complication of diagnosing a significant amount of equipment due to its long-term operation and wear and tear. Ageing increases the number of factors affecting the condition of equipment that need to be taken into account and quantitatively linked to the condition of the equipment by determining the appropriate informative correlation. However, common approaches to this definition often do not take into account the variety of diagnostic conditions and relevant time constraints, and therefore need to be developed and improved. The paper considers the diagnostics of underground pipelines as an example. The problem of promptly identifying depressurisation points in them is caused by the significant overall wear and tear of hundreds of kilometres of networks and their frequent damage. Increased wear and tear and intensified leakage repair activities increase the requirements for the accuracy and efficiency of leakage location and classification. This issue is especially acute in the post-war period. The paper describes the methods of development of leak detectors of the common correlation type, and provides an example of the corresponding technical implementation of the device. Both methods based on mutual correlation functions and entropy methods are considered. References 14, figure 1.
References
Ning Zhao, Winston T. Lin. A copula entropy approach to correlation measurement at the country level. Applied Mathematics and Computation. 2011. Vol. 218. Issue 2. Pp. 628–642. DOI: https://doi.org/10.1016/j.amc.2011.05.115.
Vladimirsky A.A., Vladimirsky I.A. Correlation parametric methods for determining the coordinates of leaks in underground pipelines. Elekrtonne modeliuvannia. 2021. Vol. 43. No 3. Pp. 3–16. DOI: https://doi.org/10.15407/emodel.43.03.003.(Ukr)
Vladimirsky A.A., Vladimirsky I.A. Spatial and Frequency Correlation Parametric Methods for Determining the Coordinates of Leaks in Underground Pipelines. Elekrtonne modeliuvannia. Vol. 43. No 4. Pp. 22–36. DOI: https://doi.org/10.15407/emodel.43.04.022. (Ukr)
Vladimirsky A.A., Vladimirsky I.A. Parametric correlation method for determining the coordinates of pipeline leaks. Patent UA No 144444, 2020. URL: https://sis.nipo.gov.ua/uk/search/detail/1456109/ (Ukr)
Skalozubov V.I., Biley D.V., Gablaia T.V., Komarov Yu.A., Klyuchnikov A.A., Foltov I.M. Development and optimization of control systems for nuclear power plants with WWER. Chernobyl: Institute for Nuclear Power Plant Safety Problems, 2008. 512 p. (Rus)
Cooper G., McGillem C. Probabilistic Methods of Signal and System Analysis. Мoskva: Мir, 1989. 376 p. (Rus)
Rewerts L.E., Roberts R., Clark M.A. Dispersion compensation in acoustic emission pipeline leak location. In Review of Progress in Quantitative Nondestructive Evaluation. Vol. 16. Pp. 427–434. New York: Plenum Press, 1997. URL: https://link.springer.com/chapter/10.1007/978-1-4615-5947-4_57 (accessed at 15.06.2025) DOI: https://doi.org/10.1007/978-1-4615-5947-4_57.
Callakot R. Diagnostics of damages. Moskva: Mir, 1989. 512 p. (Rus)
Gyzhko Yu.I., Zvaritch V.M. Features of Construction of Elements of Multilevel Expert Systems for Diagnostics of Electrical Equipment Units of Wireless System Element. Tekhnichna Elektrodynamika. 2024. No 5. Pp. 94–98. DOI: https://doi.org/10.15407/techned2024.05.094. (Ukr)
Shannon C.E. A Mathematical Theory of Communication. Bell. Syst. Tech. J. 1948. No 27. Pp. 379–423. DOI: https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.
Jaynes E.T. Information theory a nd statistical mechanics. Phys. Rev. 1957. No 106. Pp. 620–630. DOI: https://doi.org/10.1103/PhysRev.106.620.
Kullback S., Leibler L.A. On information and Sufficiency. The Annals of Mathematical Statistics. 1951. Vol. 22. Pp. 79–86. DOI: https://doi.org/10.1214/aoms/1177729694.
Yan-Ning Suna,Yun-Jie Pana, Li-Lan Liu, Zeng-Gui Gao, Wei Qin. Reconstructing causal networks from data for the analysis, prediction, and optimization of complex industrial processes. Engineering Applications of Artificial Intelligence. 2024. Vol. 138. Article no 109494. DOI: https://doi.org/10.1016/j.engappai.2024.109494.
Sun Y., Qin W., Zhuang Z. Nonparametric-copula-entropy and network deconvolution method for causal discovery in complex manufacturing systems. J. Intell. Manuf. 2022. Vol. 33. Pp. 1699–1713. DOI: https://doi.org/10.1007/s10845-021-01751-w.
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