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Updated: Jun 10, 2025

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识别直升机轴发动机非线性动态模型的智能方法
Serhii Vladov1, Arkadiusz Banasik2, Anatoliy Sachenko3,4
1Department of Scientific Work Organization and Gender Issues, Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6, Peremohy Street, 39605 Kremenchuk, Ukraine.
这项研究开发了一个准确的直升机轮轴发动机动态模型,用于关键启动和加速模式. 改进的Elman神经网络在这些短暂阶段识别发动机参数时实现了99.88%的准确性.
科学领域:
- 航空航天工程 航空航天工程
- 计算流体动力学的流体动力学.
- 人工智能的人工智能
背景情况:
- 直升机轮轴发动机主要在稳定状态模式下运行 (85%).
- 很大一部分 (15%) 运行在关键的不稳定和短暂模式,如启动和加速.
- 准确的动态建模对于这些短暂模式对于性能和安全至关重要.
研究的目的:
- 开发和增强直升机轮轴发动机的动态多模式模型.
- 为了准确地识别不稳定和短暂模式 (启动和加速) 中的发动机行为.
- 提高动态建模技术的性能和稳定性.
主要方法:
- 利用车载传感器数据 (旋转转速,气体温度,燃料消耗) 进行模型开发.
- 实现了一个改进的Elman循环神经网络与动态堆内存.
- 应用时间延迟考虑和Butterworth波器预处理到训练算法.
主要成果:
- 在启动和加速度期间识别发动机动态时达到99.88%的准确性.
- 增强的Elman网络显示性能比传统网络提高了2.7倍.
- 在120个训练时代中将模型的损失函数从2.5%降低到0.12%.
结论:
- 开发的动态模型准确地捕捉了直升机轮轴发动机在短暂模式中的行为.
- 改进的Elman神经网络为发动机监控提供了更强大的稳定性和性能.
- 通过先进的建模,这些发现有助于通过先进的建模使直升机运行更安全,更有效.
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