液体火箭发动机的智能故障诊断通过可解释的LSTM与多传感数据
Xiaoguang Zhang1, Xuanhao Hua2, Junjie Zhu3
1Xi'an Aerospace Propulsion Institute, Xi'an 710100, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
液体火箭发动机 (LREs) 的新智能故障诊断方法结合了1D-CNN和可解释的LSTM. 这种方法在识别LRE故障方面取得了很高的准确性,优于现有的方法.
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:
- 液体火箭发动机 (LREs) 在极端条件下运行,需要强大的故障诊断.
- 传统的故障检测方法与LREs复杂的动态和传感器数据作斗争.
研究的目的:
- 为LREs开发一种新的智能故障诊断系统.
- 提高LRE中故障识别的准确性和可靠性.
主要方法:
- 一种混合模型,集成一维卷积神经网络 (1D-CNN) 用于特征提取和可解释的双向长短期记忆 (LSTM) 用于时间建模.
- 使用模拟的LRE测量数据进行培训和验证.
主要成果:
- 与其他算法相比,提出的1D-CNN-LSTM方法显示出优越的故障诊断准确性.
- 实验验证证了该模型的有效性,实现了97.39%的LRE启动瞬态的故障识别精度.
结论:
- 新的1D-CNN和可解释的LSTM方法为智能LRE故障诊断提供了高度准确和有效的解决方案.
- 这种方法在确保高能系统的安全性和可靠性方面取得了重大进展.
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