研究基于模型和基于数据驱动的发动机部件故障诊断融合技术。采用极端学习机(ELM)实现基于数据驱动的故障诊断。针对极端学习机(ELM)随机选择输入层权值和隐含层偏置带来的缺点，采用改进微分进化(IDE)算法对其进行优化，减少了ELM的隐含层节点数，提高了网络的泛化能力。受到传感器数目的限制，采用基于SVD的Kalman(SVD-Kalman)滤波器实现基于模型的部件故障诊断。为了提高航空发动机部件故障诊断的精度，利用改进的迭代约简最小二乘支持向量回归机(IRR-LSSVR)算法对两种算法的估计结果在特征层进行定量融合。仿真结果表明，在发动机稳态状态下，与单独使用基于模型和数据驱动的诊断方法相比，采用特征层融合有效地提高了部件故障诊断的精度和准确率。

The model-based and data-driven-based (DDB) aero-engine component fault fusion diagnosis method is present-ed in this paper. The extreme learning machine (ELM) is used to DDB component fault diagnosis. However, the ELM input weights and hidden layer biases are generated randomly, which usually leads to more nodes in hidden layer and poor generalization ability. To overcome these drawbacks of ELM, the improved differential evolution (IDE) is used to optimize the input weights and the hidden layer biases. The optimization is carried out according to the root mean squared error (RMSE) of validation set and the norm of the output weights. Meantime, SVD-based Reduced-dimensional Kalman filters are used to estimate the health parameters which can solve the problem of limited number of sensors in model based diagnosis method. To improve the accuracy of fault diagnosis for aero-engine component, the prediction results of health parameters based on both methods are fused by Improved Re-cursive Reduced Least Squares Support Vector Regression (IRR-LSSVR). Simulation results show that compared with either the model-based or DDB approaches, the proposed fusion method improves the accuracy of fault diag-nosis significantly.