Gas path parameters play a crucial role in the health management of aeroengines, which are usually generated from the nonlinear component-level model. However, risks of stability and substantial time consumption confine online applications of conventional models. This paper proposes a mainstream parameter model that exclusively involves rotating components using fewer gas path parameters, including an adaptive strategy based on an online sequential extreme learning machine and an extended Kalman filter. The mainstream parameter model is designed in a linear parameter-varying form with a non-iterative smooth state-switching mechanism and enables real-time operation by simplifying the component complexity. Besides, some sensor measurements are employed to update rotor speeds, thus eliminating the need for derivative computations. Neural networks are introduced in compressor component calculations. Additionally, the extended Kalman filter is developed to estimate health parameters to tune the system equation residuals, and the learning machine is applied to compensate for rotating components’ pressure ratios under different degradation magnitudes. Finally, systematic tests are carried out to evaluate the computation accuracy and fast capabilities of the mainstream parameter adaptive model in various scenarios. Simulations demonstrate the proposed methodology's superiority over traditional adaptive correction schemes.

中文翻译：

---

基于 OSELM-EKF 的飞机发动机主流参数实时自适应模型


气路参数在航空发动机的健康管理中起着至关重要的作用，这些参数通常由非线性组件级模型生成。然而，稳定性风险和大量时间消耗限制了传统模型的在线应用。本文提出了一种主流参数模型，该模型专门涉及使用较少气路参数的旋转组件，包括基于在线序列极限学习机和扩展卡尔曼滤波的自适应策略。主流参数模型采用线性参数变化形式设计，具有非迭代平滑状态切换机制，通过简化组件复杂性实现实时操作。此外，一些传感器测量用于更新转子速度，因此无需进行导数计算。神经网络在压缩器组件计算中引入。此外，开发了扩展卡尔曼滤波器来估计健康参数以调整系统方程残差，并应用学习机来补偿不同退化幅度下旋转部件的压力比。最后，进行系统测试，评估主流参数自适应模型在各种场景下的计算精度和快速能力。仿真证明了所提出的方法优于传统的自适应校正方案。