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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 (k 0 ) 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.

Forecast model for estimating the service life of a diagnosed object based on the Neyman–Pearson method