Probabilistic reliability model of electrical motors in submersible well pumping units used in the uranium mining by in-situ leaching

Abstract

Relevance. A submersible electric motor is the most vulnerable component of an electric centrifugal pumping unit used in in-situ leaching uranium mining. The accuracy of calculating the need for new electric motors directly affects the profitability of the uranium production. Aim. Development of a probabilistic reliability model for a submersible electric motor that provides the calculation of its failure probability for the upcoming period of time and allows estimating with sufficient accuracy the number of motors required to replace the failed ones. Methods. Statistical methods, survival analysis, statistical hypothesis testing. Results and conclusions. The authors conducted a study of three probabilistic reliability models for electric motors of submersible well pumping units used in in-situ leaching uranium mining. As a result of the study, the maximum likelihood estimates of model parameters were determined for each model; the adequacy of the models under study was checked based on nonparametric goodness-of-fit tests. Despite the fact that the test results did not allow excluding any of the considered models, a model based on a mixture of two Weibull distributions was selected as a probabilistic reliability model demonstrating higher consistency with real data, compared to the other models. At the same time, the analysis of the components of the mixture distribution indicated the presence of a group of electric motors with an uncharacteristically low time-to-failure value compared to the average value calculated for the entire set of equipment under study, and made it possible to estimate the share of such motors, which amounted to 8% of the total population. Possible reasons explaining such heterogeneity of data, according to the authors, are hidden manufacturing defects or more severe operating conditions in which some submersible electric motors operate.

Keywords

• probabilistic reliability model
• submersible electrical motor
• uranium mining
• in-situ leaching
• mixture of Weibull distributions
• model selection