航空发动机轴承时间变化滑动评估与先前知识嵌入式双反空间时间GCN
IEEE transactions on cybernetics
|March 3, 2025
概括
这项研究引入了一个新的网络来检测航空发动机中的轴承滑行. 该方法增强了信号分析,以提高高速应用中的安全性和性能.
科学领域:
- 机械工程 机械工程
- 航空航天工程 航空航天工程
- 人工智能的人工智能
背景情况:
- 轴承滑动显著阻碍了飞机发动机向更高的速度,更低的摩擦和更低的重量发展.
- 由于弱信号,时间变化的特性和多因素合,分析轴承滑动具有挑战性.
- 有效的滑行评估需要利用多源信号来增强功能并捕捉动态行为.
研究的目的:
- 提出一种新的方法,用于对机动机中强大的轴承滑行评估.
- 改进与轴承滑动相关的微弱和时间变化的信号的分析.
- 为了提高滑行预测模型的概括性能.
主要方法:
- 开发一个先前知识嵌入式双反时空图卷积网络 (DFSTGCN).
- 利用通过动态模型,结构动态和专家经验相关联的多源信号.
- 实施双反机制,预测错误比率和不确定性损失,以提高概括性.
主要成果:
- 拟议的DFSTGCN有效地分析了时间变化的滑动数据中的空间和时间依赖性.
- 预先的知识整合增强了多源信号之间的相关性分析.
- 验证证明了该战略在各种工作条件中的有效性.
结论:
- DFSTGCN为航空发动机中准确的轴承滑行评估提供了一个有希望的方法.
- 整合先前的知识和双反机制可以提高模型的稳定性和通用性.
- 这种方法解决了分析复杂轴承滑动现象的关键挑战.
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