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一个基于注意力的双阶梯级变压器为轮风扇发动机剩余的使用寿命预测
Zhengyang Fan1, Wanru Li1, Kuo-Chu Chang1
1Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
准确的飞机发动机剩余使用寿命 (RUL) 预测至关重要. 一个新的STAR框架具有两阶段的注意力和层次结构,比现有的变压器模型改进了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) 估计对于飞机发动机安全和运营连续性至关重要.
- 变压器架构显示出使用传感器数据进行RUL预测的前景,但往往缺乏传感器方面的注意力.
- 现有的仅关注时间注意力的方法可能会导致RUL预测的不准确性.
研究的目的:
- 为改进飞机发动机RUL预测引入一种新的基于注意力的两阶段层次变压器 (STAR) 框架.
- 通过结合时间和传感器智能的注意力机制来解决当前变压器模型的局限性.
- 通过利用分层编码器-解码器结构来提高RUL预测的准确性,以获取多个尺度的时间信息.
主要方法:
- 开发STAR框架,采用两阶段的注意力机制.
- 在STAR框架内集成分层编码器-解码器结构.
- 使用来自CMAPSS数据集的多变量时间序列传感器数据进行系统评估.
主要成果:
- 拟议的STAR框架在RUL预测任务中表现出卓越的性能.
- 星星显著优于现有的最先进的基于变压器的模型.
- 两阶段的注意力和层次结构有效地捕捉了复杂的时间依赖性和传感器相互作用.
结论:
- 在飞机发动机RUL预测方面,STAR框架提供了显著的进步.
- 将传感器的注意力与时间注意力相结合,对于准确的RUL估计至关重要.
- 层次结构增强了模型在不同时间尺度上处理信息的能力,从而提高了预测准确度.
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