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基于数据驱动方法的高精度预测压缩机几何变量系统的方法.

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  • 1Civil Aviation Flight University of China, Guanghan, 618300, China.

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概括
此摘要是机器生成的。

本研究介绍了一种增强的ConvNeXt模型,用于预测航空发动机压缩机系统的滑动窗口算法. 这种新的方法通过结合未来的非目标参数数据来减少预测错误,提高了航空发动机健康监测的准确性.

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科学领域:

  • 航空航天工程 航空航天工程
  • 人工智能的人工智能
  • 机械工程 机械工程

背景情况:

  • 了解航空发动机压缩机系统对于完整的发动机建模和维护至关重要.
  • 传统的预测方法难以预测故障,因为它们无法利用来自非目标参数的未来信息.
  • 准确的故障诊断和预测对于确保航空发动机的可靠性和安全性至关重要.

研究的目的:

  • 提出一个增强的ConvNeXt模型,用于预测空气发动机中的压缩机几何变量系统.
  • 通过结合非目标参数的未来信息来解决传统预测技术的局限性.
  • 为了缓解更长的预测时间的预测错误增加的问题.

主要方法:

  • 开发一个增强的ConvNeXt模型,与滑动窗口算法集成.
  • 在预测模型中利用非目标参数的未来信息.
  • 在各种航空发动机状态上进行实验,包括过渡和稳定状态,使用真实飞行数据.

主要成果:

  • 提出的方法显著缓解了随着预测长度的增加而增加的预测误差的问题.
  • 与在特定条件下的标准预测方法相比,预测误差减少到20.07%.
  • 证明了该方法在各种类型的航空发动机中的适用性.

结论:

  • 增强的ConvNeXt模型与滑动窗口算法为预测航空发动机压缩机系统提供了可行和有效的方法.
  • 该方法能够纳入未来的非目标参数数据,提高了预测准确性和可靠性.
  • 使用真实飞行数据进行的实验验证证证了该模型对各种航空发动机运行状态的可信性.