| Citation: | Zhou Deqing, Liu Yi, Yang Junjie, et al. Research on the parameterized proxy model of surface wear of sliding bearings in aviation fuel gear pumps[J]. Journal of Aerospace Power, 2026, 41（9）：20240795 doi: 10.13224/j.cnki.jasp.20240795
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The wear and degradation of sliding bearings is one of the important factors affecting the life and reliability of aviation fuel pumps. The numerical simulation model based on this object sacrifices computational efficiency while continuously improving the fitting degree with test results, which is not conducive to its application in engineering practice. This article proposes a method of parameterizing the wear amount and establishing a Gaussian process model. By parameterizing the circumferential distribution of wear amount at different speeds, eccentricities, and wear times, the input-output parameters are simplified, and a Gaussian process regression model with much higher computational efficiency than traditional simulation methods is constructed. On this basis, the error and confidence level of the model prediction results were studied under different training sets, operating conditions, and wear times, and the accuracy of the model was verified by comparing the test results with the model prediction. Research has found that selecting an appropriate training set can partially improve the predictive performance of the model, manifested as a narrowing of the 95% confidence interval; When the journal speed, eccentricity, and wear time are used as independent variables, the results provided by the prediction model are highly consistent with the simulation model, with a maximum relative error of 5.26%. Furthermore, by comparing the model predictions with test results under the same operating conditions, it was found that the relative error was less than 5%, which meets the accuracy requirements and validates the rationality of the prediction model. The rationality of the prediction model is verified; When using wear time as the independent variable, the average absolute error is the lowest, only 0.001 µm, and when using eccentricity as the independent variable, it is the highest, at 1.33 µm; Further comparative studies have found that the prediction errors of the model come from two aspects: systematic errors in the parameterization process and systematic errors in the Gaussian process regression model. The dominant factors of prediction errors under different operating conditions and wear times have been analyzed.
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