飞机发动机剩余使用寿命预测的双频增强注意网络
Qichao Yang1, Baoping Tang1, Qikang Li1
1The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, PR China.
ISA transactions
|July 9, 2023
概括
这项研究引入了预测飞机发动机剩余使用寿命 (RUL) 的新框架. 先进的双频增强注意网络提高了RUL预测的准确性和可靠性,以实现更安全的操作.
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:
- 准确的剩余使用寿命 (RUL) 预测对于飞机发动机安全和维护至关重要.
- 现有的方法可能会与复杂的降解模式和传感器数据冗余性作斗争.
研究的目的:
- 提出一个新的飞机发动机预测框架 RUL.
- 通过使用先进的深度学习技术来提高预测的准确性和稳定性.
主要方法:
- 开发了一种使用可分离卷积神经网络的双频增强注意力网络架构.
- 引入了信息量标准 (IVC) 和信息内容值 (CIT),用于量化传感器数据和删除冗余.
- 集成可训练的福里埃变换 (FMB-f) 和波形变换 (FMB-w) 模块,用于捕获降解趋势.
- 利用高效的道注意力阻断来模拟传感器间的依赖关系.
主要成果:
- 拟议的框架在实验评估中证明了准确的RUL预测.
- 频率增强模块有效地捕捉了全球趋势和本地退化细节.
- 注意力机制通过考虑传感器相互依赖,提高了预测稳定性和精度.
结论:
- 新的RUL预测框架在飞机发动机预测方面取得了重大进展.
- 集成频率增强模块和注意力机制可以提高预测性能和稳定性.
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