Классификация уровня эрозии проточной части изолированной ступени осевого компрессора

Abstract

Erosive wear of the parts of the gas path of an axial compressor of a gas turbine is a common reason for premature decommissioning of equipment. The creation of an advanced diagnostic system, which will allow determining the level of blade erosion according to standard parameters without the inspection or disassembly, is topical for Russian gas transmission enterprises. The paper presents preliminary results of applying machine learning methods to solve such a problem for an isolated stage of an axial compressor. The verified results of numerical simulation of the air flow in the stage were used as initial data. The degree of erosion was set as the ratio of the chord of the eroded blade to the chord of the new blade in the peripheral section. The same parameter was the target for machine learning models. Sets of local and integral parameters of the numerical calculation were used as parameters. As a result of the primary study, the random forest model showed the best results when using all available parameters and the parameters with the highest correlation. Conclusions are formulated about the applicability of machine learning methods for creating a model for assessing the degree of erosion. The development of the work is connected with the creation of a model for predicting the technical condition of the flow path of the entire compressor.