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航空发动机振动信号的趋势预测方法,结合了使用多操作条件下的长短期内存进行增强的切片级自适应规范化
Jiantao Lu1, Kuangzhi Yang1, Peng Zhang2
1College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210001, China.
本研究引入了一种增强的切片级自适应规范化 (SAN) 与长短期内存 (LSTM) 用于飞机发动机趋势预测. 该方法提高了早期异常警告的预测准确性,提高了安全性.
科学领域:
- 航空航天工程 航空航天工程
- 机械工程 机械工程
- 数据科学数据科学数据科学
背景情况:
- 飞机发动机故障构成重大安全风险.
- 准确的趋势预测和早期异常检测对于防止事故至关重要.
- 现有的方法可能会与非静止数据和局部过拟合作斗争.
研究的目的:
- 为飞机发动机提出先进的趋势预测方法.
- 提高早期异常预警的准确性和可靠性.
- 为了解决当前规范化和预测技术的局限性.
主要方法:
- 开发了一种条件识别技术,用于分类操作状态 (静止,启动,极限).
- 应用了增强的切片级自适应规范化 (SAN) 与L1过来处理非静止信号并减少过.
- 集成了一个L1过器用于趋势提取和量化估计的切片长度与尾部修改.
- 构建了一个长期短期记忆 (LSTM) 神经网络,用于预测规范化数据.
主要成果:
- 与现有方法相比,拟议的方法显示出更高的预测准确性.
- 实现了有效减少信号波动,噪声和过度装配.
- 增强的SAN方法成功地减轻了振动信号中的非静止因素.
- 尾部修改技术扩大了标准化技术的适用范围.
结论:
- 增强的SAN-LSTM方法为飞机发动机趋势预测提供了强大的方法.
- 该技术为早期异常检测提供了更高的准确性,有助于提高航空安全.
- 这种方法在多种操作条件下有效,显示了它的多功能性.
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