Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method
The paper is aimed at developing a forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method. It presents a procedure for selecting necessary and sufficient number of diagnostic indicators using the forecast model. The technique has been tested on the ba...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201821603011 |
| _version_ | 1818964600063787008 |
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| author | Barsukov Sergey Pakhomov Sergey |
| author_facet | Barsukov Sergey Pakhomov Sergey |
| author_sort | Barsukov Sergey |
| collection | DOAJ |
| description | The paper is aimed at developing a forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method. It presents a procedure for selecting necessary and sufficient number of diagnostic indicators using the forecast model. The technique has been tested on the basis of a power transformer with a liquid dielectric. A condition-based operation strategy has been proposed for the transformer. According to this strategy, the iron impurity content in the dielectric liquid (oil) of the transformer should be measured every year of operation. Based on the forecast model, it is possible to calculate the variation of average risk (R) and a threshold value of iron impurity content in the transformer oil (k0) for each year of operation. Using these parameters, a reliable forecast model can be constructed to estimate the remaining service life of the transformer. The obtained relationships make it possible to identify a scientifically grounded stage in the service life of a diagnosed object, at which the number of measurable diagnostic indicators (indicators that are necessary for assessing the real technical condition of equipment) can be minimized. |
| first_indexed | 2024-12-20T13:03:41Z |
| format | Article |
| id | doaj.art-50729f8ae6d94ace90327663c5fd6e00 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-20T13:03:41Z |
| publishDate | 2018-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-50729f8ae6d94ace90327663c5fd6e002022-12-21T19:39:50ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012160301110.1051/matecconf/201821603011matecconf_pts2018_03011Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson methodBarsukov SergeyPakhomov SergeyThe paper is aimed at developing a forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method. It presents a procedure for selecting necessary and sufficient number of diagnostic indicators using the forecast model. The technique has been tested on the basis of a power transformer with a liquid dielectric. A condition-based operation strategy has been proposed for the transformer. According to this strategy, the iron impurity content in the dielectric liquid (oil) of the transformer should be measured every year of operation. Based on the forecast model, it is possible to calculate the variation of average risk (R) and a threshold value of iron impurity content in the transformer oil (k0) for each year of operation. Using these parameters, a reliable forecast model can be constructed to estimate the remaining service life of the transformer. The obtained relationships make it possible to identify a scientifically grounded stage in the service life of a diagnosed object, at which the number of measurable diagnostic indicators (indicators that are necessary for assessing the real technical condition of equipment) can be minimized.https://doi.org/10.1051/matecconf/201821603011 |
| spellingShingle | Barsukov Sergey Pakhomov Sergey Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method MATEC Web of Conferences |
| title | Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method |
| title_full | Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method |
| title_fullStr | Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method |
| title_full_unstemmed | Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method |
| title_short | Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method |
| title_sort | forecast model for estimating the service life of a diagnosed object based on the neyman pearson method |
| url | https://doi.org/10.1051/matecconf/201821603011 |
| work_keys_str_mv | AT barsukovsergey forecastmodelforestimatingtheservicelifeofadiagnosedobjectbasedontheneymanpearsonmethod AT pakhomovsergey forecastmodelforestimatingtheservicelifeofadiagnosedobjectbasedontheneymanpearsonmethod |