针对柴油机性能预测中高维特征筛选困难与小样本数据分布不均衡问题，提出结合数据增强与机器学习的联合预测框架。首先，采用BorutaShap算法从48个特征中筛选关键参数，缓解因主观特征选择导致的模型泛化能力不足问题；其次，针对燃油消耗率和最高燃烧压力数据的小样本缺陷，使用WGAN-GP模型，通过动态梯度惩罚增强样本的分布合理性；最后，对比WGAN-GP、SMOTE和VQ-VAE模型生成数据在XGBoost、SVR、RF、GBDT 4种模型中的预测性能，以验证方法的有效性。结果表明，WGAN-GP生成数据使XGBoost对燃油消耗率预测的决定系数R²提升至0.985（MAE=1.659 g/(kW·h)），对最高燃烧压力预测的决定系数R²达0.943（MAE=0.006 MPa），较SMOTE/VQ-VAE方法，R²提升了5%～8%。综合而言，本研究通过特征选择和数据增强结合的方式，有效解决了在多样本特征和小数据样本下的柴油机性能预测问题。

A joint prediction framework of data augmentation and machine learning was proposed to address the challenges of high-dimensional feature selection and imbalanced small-sample data in diesel engine performance prediction. The BorutaShap algorithm was first used to select key parameters from 48 features, which reduced the generalization limitations caused by subjective feature selection. Then a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) was applied to address the small-sample problems in brake-specific fuel consumption (BSFC) and peak cylinder pressure (PCP). The distribution rationality of samples was enhanced by dynamic gradient punishment. The predictive performances of data generated by WGAN-GP, SMOTE, and VQ-VAE models were finally compared across 4 models of XGBoost, SVR, RF, and GBDT to validate the effectiveness of the method. The results showed that WGAN-GP improved the determination coefficient R² of XGBoost to 0.985 (MAE=1.659 g/(kW·h)) for BSFC and 0.943 (MAE=0.006 MPa) for PCP. Accuracy improvements of 5%-8% were achieved over SMOTE and VQ-VAE. In summary, the integration of feature selection and data augmentation effectively addressed performance prediction challenges of diesel engine under diverse feature characteristics and limited data samples.