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航空发动机效率高的频道注意门式图形变压器 剩余使用寿命预测
Chuang Chen1,2, Mengchen Li1, Jiantao Shi1
1College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China.
ACS omega
|December 1, 2025
概括
本研究介绍了EGG-变压器,用于准确的航空发动机剩余使用寿命 (RUL) 预测. 该模型通过有效捕捉退化模式和提高飞行安全,增强了RUL预测.
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
- 机器学习 机器学习
背景情况:
- 准确的剩余使用寿命 (RUL) 估计对于航空发动机的安全和维护至关重要.
- 当前的深度学习方法与传感器数据中的局部顺序依赖性和长期退化模式作斗争.
研究的目的:
- 开发一个先进的预测框架,用于在航空发动机中进行可靠的RUL估计.
- 解决局部依赖性和渐进性退化行为的建模方面的局限性.
主要方法:
- 推出了高效通道注意力 (ECA) 开关图形变压器 (EGG-变压器).
- 组合图形卷积网络 (GCNs) 用于局部结构学习.
- 采用了ECA开关的融合模块,用于自适应性功能合并.
- 使用基于变压器的编码器进行远程时间模式分析.
主要成果:
- 在C-MAPSS数据集上,EGG-变压器表现出卓越的性能.
- 实现了平均根平均平方误差 (RMSE) 的22.85%的减少.
- 与领先的方法相比,将得分指数降低了10.21%.
结论:
- 拟议的EGG-Transformer框架为RUL预测提供了更高的准确性和稳定性.
- 该方法有效地模拟了航空发动机传感器数据中的局部和远程依赖.
- 突出了提高航空发动机维护和安全的实际适用性.
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