Cumulative Sum Algorithms for Automatic Detection of Gas Well Parameter Changes
| Parent link: | IFAC-PapersOnLine Vol. 50, iss. 1 : 20th IFAC World Congress, July 2017.— 2017.— [P. 14614-14619] |
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| مؤلفون مشاركون: | , |
| مؤلفون آخرون: | , , , |
| الملخص: | Title screen The problem of the change point detection in a sequence of random variables is considered. The task arises in control of technological processes, particularly, in oil and gas production management. Some equipment parameters are to be controlled in order to detect a change of the equipment characteristics and, consequently, a breakdown of its technological regime. As a rule, the data observed are stochastic with the unknown distribution. In the paper a non-parametric method for detection a change in the mean or in the variance of data is developed and some modifications are proposed. All the algorithms do not use the information concerning the distribution function of observations before and after the change point. The algorithms are applied to detect change points of the characteristics of a gas well. Режим доступа: по договору с организацией-держателем ресурса |
| منشور في: |
2017
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://doi.org/10.1016/j.ifacol.2017.08.1334 |
| التنسيق: | الكتروني فصل الكتاب |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=667215 |
| الملخص: | Title screen The problem of the change point detection in a sequence of random variables is considered. The task arises in control of technological processes, particularly, in oil and gas production management. Some equipment parameters are to be controlled in order to detect a change of the equipment characteristics and, consequently, a breakdown of its technological regime. As a rule, the data observed are stochastic with the unknown distribution. In the paper a non-parametric method for detection a change in the mean or in the variance of data is developed and some modifications are proposed. All the algorithms do not use the information concerning the distribution function of observations before and after the change point. The algorithms are applied to detect change points of the characteristics of a gas well. Режим доступа: по договору с организацией-держателем ресурса |
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| DOI: | 10.1016/j.ifacol.2017.08.1334 |