| Citation: | CAI Shuyu, KUANG Wentao. Aeroengine baseline prediction model based on improved ADDA[J]. Journal of Aerospace Power, 2025, 40（8）：20240346 doi: 10.13224/j.cnki.jasp.20240346
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The challenge of low prediction accuracy in cross-model aeroengine baseline transfer prediction with existing domain adaptation methods was addressed by proposing a novel prediction model based on improved adversarial discriminative domain adaptation (ADDA). The proposed approach incorporated Transformer architecture and self-attention mechanisms to extract long-term features from aeroengine performance data, and enhanced the model’s capacity to capture dynamic features. Additionally, in the domain adversarial module, maximum mean discrepancy and information noise contrast estimation optimization structures were introduced to better leverage the information from limited input data and mitigate interference from redundant information. Consequently the model achieved enhanced accuracy in cross-model aeroengine baseline prediction. Experimental results demonstrated that the improved ADDA model reduced the mean absolute error and root mean square error of baseline prediction for engine gas temperature by 19.3% and 16.2% respectively, while increasing the coefficient of determination by 4.4%. For the baseline of fuel flow, the mean absolute error and root mean square error were reduced by 26.8% and 30.1%, while the coefficient of determination increased by 6.4%. For the baseline of high-pressure rotor speed, the mean absolute error and root mean square error were reduced by 19.6% and 20.0%, while the coefficient of determination increased by 6.5%. This improvement enabled more precise prediction of baselines across different aeroengine models.
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