Modeling of neutron flux density anomalies in the automated system of «Shelter» Object’s nuclear safety control.
DOI:
https://doi.org/10.34121/1028-9763-2021-4-70-77Keywords:
Shelter Object, neutron flux density, dynamics analysis, parametric methods, statistical criteria, detection of anomalies, об’єкт «Укриття», щільність потоку нейтронів, аналіз динаміки, параметричні методи, статистичні критерії, виявлення аномалійAbstract
Abstract. Analysis of the dynamics of neutron flux density (NFD) from fuel-containing masses (FCM) in the «Shelter» Object shows the presence of values that exceed the average values for different observation periods. Identification of such values by the criterion of «anomaly / non-anomaly» will allow excluding uninformative events from the array of observations. Or, in the case of the anomaly confirmation, it will allow forming effective actions for decision-making in order to eliminate the consequences of such events. To solve the problem of detecting anomalous measurements, now there is utilized the theory of statistical solutions which is based on the use of parametric methods. The utilization of these methods requires a priori information about the nature of the distribution of the measured process and its parameters. In order to find an effective solution to the problem of detecting and eliminating anomalous measurements, it is necessary to know the statistical characteristics of normal and anomalous components. This paper proposes statistical criteria for estimating anomalies in time series of NFD which have different approaches to the formation of observation intervals, power and reliability of anomaly detection. Depending on the type of distribution of the array of observations, a set of criteria is proposed. These criteria are most expedient to use when checking the anomaly of series levels for the exponential distribution, the Poisson and Weibull distribution. The capacity of the criteria has been evaluated depending on the sample size. The article defines the accuracy of determination of anomalies by criteria with the known values of the mean and dispersion in the studied sample. As the result, it is recommended to use the Grubbs test to study the anomaly of the sample levels with n>700, and the Dixon and Smolyak-Titarenko criteria for the samples with n<50. The utilization of optimal criteria depending on the characteristics of the studied samples will increase the mathematical significance of the obtained results and, as a result, will improve the quality of management decisions and nuclear safety on the “Shelter” Object as a whole.References
1. Об’єкт «Укриття» в умовах нового безпечного конфайнмента: монографія / за ред. А.В. Носовського. Чорнобиль: ІПБ АЕС НАН України, 2021. 344 с.
2. Lytvynov V., Stoianov N., Skiter I., Lytvyn S. Traffic Abnormalities Identification Based on the Stationary Parameters Estimation and Wavelet Function Detailization. Mathematical Modeling and Simulation of Systems. MODS 2019. Advances in Intelligent Systems and Computing. Springer, Cham, 2019. Vol. 1019. P. 83–95.
3. Скітер І.С. Використання EWMA-статистики для виявлення аномальної поведінки комп’ютерної мережі. Створення та модернізація озброєння і військової техніки в сучасних умовах: зб. тез доп. 16 наук.-техн. конф. Чернігів: ДНВЦ ЗС України, 2016. С. 18–21.
4. NCSS [Statistical software]. (n.d.). NCSS 2021. Data Analysis & Graphics: Time Series and Forecasting Methods in NCSS. Retrieved 3 February. 2021. URL: https://www.ncss.com/software/ncss/time-series-and-forecasting-in-ncss.
5. Долгіх А.О., Байбуз О.Г. Аналіз методів, моделей та програмних засобів прогнозування часових рядів. Відкриті інформаційні та комп’ютерні інтегровані технології: зб. наук. праць Національного аерокосмічного університету ім. Жуковського «ХАІ». Харків, 2018. Т. 79. С. 74–87.
6. Смоляк С.А., Титаренко Б.П. Устойчивые методы оценивания. М.: Статистика, 1980. 208 c.
7. Бродский Я.С., Быцань Н.Н., Власенко В.М. Об исключении экстремальных значений. Заводские лаборатории. 1975. № 7. C. 847–849.
8. Kimber A.C., Stevens H.J. The null distribution of a test for two upper outliers in an exponential sample. Applied Statistics. 1987. Vol. 30. P. 153–157.
9. Fung Karen Yuen, Paul S.R. Comparisons of outlier detection procedures in Weibull or extreme-value distributions. Communications in Statistics – Simulation and Computation. 1985. Vol. 14, N 4. P. 895–917.
10. Grubbs F.E. Simple criteria for testing Rejection of Outlying observations. Proc. of the American Mathematical Society. 1950. Vol. 21. P. 27–58.
11. Dixon W.J. Rations involving extreme Values. Proc. of the American Mathematical Society. 1956. Vol. 22. P. 68–76.
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