Real-time adaptive model of mainstream parameters for aircraft engines based on OSELM-EKF

IF 5 1区 工程技术 Q1 ENGINEERING, AEROSPACE
Yingchen Guo , Jiazhu Teng , Xin Zhou , Zelong Zou , Jinquan Huang , Feng Lu
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Abstract

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.
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,只涉及旋转部件,使用较少的气体路径参数,包括基于在线顺序极限学习机和扩展卡尔曼滤波器的自适应策略。主流参数模型以线性参数变化形式设计,采用非迭代平滑状态切换机制,通过简化组件复杂性实现实时运行。此外,一些传感器测量值可用于更新转子速度,因此无需进行导数计算。在压缩机组件计算中引入了神经网络。此外,还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的旋转部件压力比。最后,还进行了系统测试,以评估主流参数自适应模型在各种情况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修正方案。
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来源期刊
Aerospace Science and Technology
Aerospace Science and Technology 工程技术-工程:宇航
CiteScore
10.30
自引率
28.60%
发文量
654
审稿时长
54 days
期刊介绍: Aerospace Science and Technology publishes articles of outstanding scientific quality. Each article is reviewed by two referees. The journal welcomes papers from a wide range of countries. This journal publishes original papers, review articles and short communications related to all fields of aerospace research, fundamental and applied, potential applications of which are clearly related to: • The design and the manufacture of aircraft, helicopters, missiles, launchers and satellites • The control of their environment • The study of various systems they are involved in, as supports or as targets. Authors are invited to submit papers on new advances in the following topics to aerospace applications: • Fluid dynamics • Energetics and propulsion • Materials and structures • Flight mechanics • Navigation, guidance and control • Acoustics • Optics • Electromagnetism and radar • Signal and image processing • Information processing • Data fusion • Decision aid • Human behaviour • Robotics and intelligent systems • Complex system engineering. Etc.
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