适应轻量级的时空信息-提取-基于操作者的DL方法用于航空发动机RUL预测
Junren Shi1, Jun Gao2, Sheng Xiang1
1School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
这项研究介绍了Involution GRU (Inv-GRU),这是一种用于准确预测航空发动机剩余使用寿命 (RUL) 的新型深度学习模型. Inv-GRU增强了时空特征提取,提高了RUL预测的准确性,并减少了计算负载.
科学领域:
- 航空航天工程 航空航天工程
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 准确的剩余使用寿命 (RUL) 预测对于航空航天领域的运营安全和经济效率至关重要.
- 现有用于RUL预测的时空空间模型通常存在结构复杂性和有限的适应性特征提取问题.
- 对于处理航空发动机中复杂降解数据的高效和适应性方法的需求至关重要.
研究的目的:
- 提出一种新的,轻量级的深度学习框架,用于增强航空发动机RUL预测.
- 引入Involution GRU (Inv-GRU) 作为适应时空特征提取的操作员.
- 提高RUL预测模型的准确性和降低计算负担.
主要方法:
- 开发了Involution GRU (Inv-GRU),通过将一个involution运算符集成到一个Gated Recurrent Unit (GRU) 中,用于适应的时空特征提取.
- 构建了一个基于Inv-GRU的深度学习框架来处理多个原始飞机发动机数据,生成健康指标 (HI).
- 使用完全连接的层进行尺寸缩小和RUL回归,基于提取的HI,在C-MAPSS数据集上验证.
主要成果:
- 基于Inv-GRU的框架成功地预测了C-MAPSS数据集上的航空发动机RUL.
- 与现有方法相比,RUL预测准确度被证明更高.
- 显示了计算负担的显著减少,突出显示了该模型的效率.
结论:
- 拟议的Inv-GRU操作员有效地提取降解信息,并增强用于航空发动机RUL预测的时空处理.
- 基于Inv-GRU的深度学习框架为RUL预测提供了一个计算效率高,准确的解决方案.
- 这种方法在提高飞机发动机操作的安全性和可靠性方面具有重大前景.
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