基于Hessian局部线性嵌入和MLP-Mixer的液体火箭发动机涡轮泵轻量化故障诊断框架

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摘要作为液体火箭发动机推进剂输送系统的关键部件，涡轮泵的运行状态直接影响着整个运载系统的性能，然而，现有的故障诊断方法往往面临特性参数选择片面及计算复杂度高等问题。针对上述局限，提出了面向涡轮泵的轻量化故障诊断框架。所提方法利用Hessian局部线性嵌入算法对信号时域、频域及时频特征进行降维，并引入一种轻量化的深度学习模型MLP-Mixer作为分类器，进而实现不同故障状态的辨识。采用某型号涡轮泵试车数据验证了所提方法的有效性，结果表明本文方法能够在保障诊断精度的同时有效降低计算复杂度，提高诊断效率。

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	窦唯1
	赵东方2
	张宏利2
	刘树林2

Abstract：As the key component of liquid rocket engine propellant delivery system, the operation state of turbopump directly affects the performance of the entire launch system. However, the existing fault diagnosis methods often suffer from the problems of one-sided selection of feature parameters and high computational complexity. Aiming at the above limitations, a lightweight fault diagnosis framework towards turbopump is proposed. The proposed method uses the Hessian local linear embedding algorithm to reduce the dimension of the time-domain, frequency-domain and time-frequency features of the signal, and introduces a lightweight deep learning model MLP-Mixer as the classifier to realize the identification of different fault states. The validity of the proposed method is verified by the test run data of a turbopump. The results show that the proposed method can effectively reduce the computational complexity and improve the diagnostic efficiency while ensuring the diagnostic accuracy.

收稿日期: 2023-01-15 出版日期: 2024-01-28

引用本文:

窦唯1，赵东方2，张宏利2，刘树林2. 基于Hessian局部线性嵌入和MLP-Mixer的液体火箭发动机涡轮泵轻量化故障诊断框架[J]. 振动与冲击, 2024, 43(2): 156-165.
DOU Wei1,ZHAO Dongfang2,ZHANG Hongli2,LIU Shulin2. Lightweight fault diagnosis framework towards a liquid rocket engine turbopump based on Hessian locally linear embedding and MLP-Mixer. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(2): 156-165.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I2/156

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