Lightweight fault diagnosis framework towards a liquid rocket engine turbopump based on Hessian locally linear embedding and MLP-Mixer

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (2) : 156-165.

DOU Wei1,ZHAO Dongfang2,ZHANG Hongli2,LIU Shulin2

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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.

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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[J]. Journal of Vibration and Shock, 2024, 43(2): 156-165

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