A multi-source-information-constrained method for identifying component performance dispersion in mass-produced aero-engines

During the mass production of aero-engines, variations in manufacturing processes, material properties, and assembly precision inevitably cause deviations in component performance, leading to noticeable dispersion in overall delivered engine performance. However, the limited number of measurement stations and the insufficient information content of single-engine test data make it difficult for traditional methods to reliably identify component-level deviations or characterize performance dispersion across production batches. To address this challenge, this study proposes a multi-source-information-constrained identification method for quantifying component performance dispersion in mass-produced aero-engines. The method integrates condition-number-based screening of measurement parameters, mean - value rationality constraints derived from multi-engine statistics, and prior dispersion estimates from a geometry–component correlation model. These constraints collectively mitigate the effects of measurement noise and systematic bias and enhance the robustness of the identification process.