基于人工神经网络的航空发动机转子系统损伤参数统计特征分析

Jianpeng Chen, L. Xie, Bingfeng Zhao, Hongqiu Wu, Xiaoyu Yang
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引用次数: 0

摘要

航空发动机是现代航空航天飞机的核心部件之一,其旋翼系统的性能和可靠性是保证航空航天飞机安全运行的关键。本文建立了航空发动机转子系统的有限元模型,分析了离心载荷、气动载荷、温度载荷和旋转加速度共同作用下航空发动机转子系统的载荷状态。此外,考虑到转子系统有限元分析所需的时间,建立了基于人工神经网络的航空发动机关键部件损伤参数分析代理模型,并利用该模型快速获得了关键部件损伤参数的统计特征。结果表明,所建立的基于人工神经网络的航空发动机转子系统损伤参数替代模型具有较好的预测精度和预测效率,可为大型复杂机械的在线状态监测和可靠性评估提供实用的技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical Characteristics Analysis of Damage Parameters of Aero-Engine Rotor System Based on Artificial Neural Network
Aero-engine is one of the core components of modern aerospace aircraft, and the performance and reliability of its rotor system are crucial to ensure the safe operation of aerospace aircraft. In this paper, a finite element model of the aero-engine rotor system is established to analyze the load state of the aero-engine rotor system under the combined effects of centrifugal load, aerodynamic load, temperature load, and rotational acceleration. In addition, considering the time required for the finite element analysis of the rotor system, a surrogate model based on an artificial neural network is developed to analyze the damage parameters of the key components of the aero-engine and the statistical characteristics of the damage parameters of the key components are quickly obtained using the developed surrogate model. The results show that the proposed artificial neural network-based surrogate model for damage parameters of the rotor system of aero-engines has good prediction accuracy and efficiency and can provide practical technical support for online condition monitoring and reliability assessment of large and complex machinery.
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