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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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.

Received: 15 January 2023 Published: 28 January 2024

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	Articles by authors
	DOU Wei1
	ZHAO Dongfang2
	ZHANG Hongli2
	LIU Shulin2

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DOU Wei1,ZHAO Dongfang2,ZHANG Hongli2, et al. 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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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I2/156

[1] 张振臻, 陈晖, 高玉闪, 等. 液体火箭发动机故障诊断技术综述[J]. 推进技术, 2022, 43 (6): 20-38. ZHANG Zhenzhen, CHEN Hui, GAO Yushan, et al. Review on fault diagnosis technology of liquid rocket engine[J]. Journal of Propulsion Technology, 2022, 43 (6): 20-38. [2] 戴屹梅, 张和生, 方柯. 基于导航定位原理的火箭涡轮泵轴承故障诊断[J]. 宇航学报, 2019, 40 (3): 286-294. DAI Yimei, ZHANG Hesheng, FANG Ke. Fault diagnosis of rocket turbine pump bearing based on navigation and position principle[J]. Journal of Astronautics, 2019, 40 (3): 286-294. [3] Satoh D, Tsutsumi S, Hirabayashi M. Estimating model parameters of liquid rocket engine simulator using data assimilation[J]. Acta Astronautica, 2021, 177: 373-385. [4] PAN H Y, XU H F, ZHENG J D. Multi-class fuzzy support matrix machine for classification in roller bearing fault diagnosis[J]. Advanced Engineering Informatics, 2022, 51: 101445. [5] 王兴, 张晗, 朱家正, 等. 多头注意力驱动的航空高速轴承故障诊断方法[J]. 振动与冲击, 2023, 42 (4): 295-305. WANG Xing, ZHANG Han, ZHU Jiazheng, et al. A fault diagnosis method for aviation high-speed bearings driven by multi-head attention[J]. Journal of Vibration and Shock, 2023, 42 (4): 295-305. [6] 邵海东, 肖一鸣, 颜深.仿真数据驱动的改进无监督域适应轴承故障诊断[J]. 机械工程学报, 2023, 59 (3): 76-85. SHAO Haidong, XIAO Yiming, YAN Shen. Simulation data-driven enhanced unsupervised domain adaptation for bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2023, 59 (3): 76-85. [7] 杨懿, 陈文丽, 王永鹏, 等. 基于Hilbert-Huang变换的液体火箭发动机涡轮泵故障分析[J].清华大学学报(自然科学版), 2022, 62 (3): 540-548. YANG Yi, CHEN Wenli, WANG Yongpeng, et al. Fault analyses of liquid rocket engine turbopumps based on Hilbert-Huang transforms[J]. Journal of Tsinghua University (Science and Technology), 2022, 62 (3): 540-548. [8] 钟福利,李辉.基于提升小波与RLS的涡轮泵故障检测方法[J]. 载人航天, 2013, 19 (5): 30-36. ZHONG Fuli, LI Hui. A fault detection method for turbopump based on lifting wavelet an RLS[J]. Manned Spaceflight, 2013, 19 (5): 30-36. [9] CHA J, KO S, Park S Y. Fault detection and diagnosis algorithms for transient state of an open-cycle liquid rocket engine using nonlinear Kalman filter methods[J]. Acta Astronautica, 2019, 163: 147-156. [10] LI X M, SUN J Y. A novel method for diagnosing rolling bearing faults based on the frequency spectrum distribution of the modulation signal[J]. Measurement Science and Technology, 2022, 33: 085003. [11] 赵小强, 张毓春. 基于双路并行多尺度ResNet的滚动轴承故障诊断方法[J]. 振动与冲击, 2023, 42 (3):199-208. ZHAO Xiaoqiang, ZHANG Yuchun. Fault diagnosis method of rolling bearing based on dual-path parallel multi-scale ResNet method[J]. Journal of Vibration and Shock, 2023, 42 (3):199-208. [12] LI W, ZHONG X, SHAO H D, et al. Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework[J]. Advanced Engineering Informatics, 2022, 52: 101552. [13] LI X, MA Z Q, KANG D. Fault diagnosis for rolling bearing based on VMD-FRFT[J]. Measurement, 2020,155: 107554. [14] 田路, 田干, 张炜, 等. 基于支持向量机的涡轮泵故障诊断方法研究[J]. 控制工程, 2007 (S2): 138-140. TIAN Lu, TIAN Gan, ZHANG Wei , et al. Turbo Pump Fault Diagnosis Based on Support Vector Machine[J]. Control Engineering of China, 2007 (S2): 138-140. [15] 黄志祥, 张炜, 杨正伟,等.改进BP算法用于液体发动机涡轮泵故障诊断[J]. 控制工程, 2007 (S2): 144-146. HUANG Zhixiang, ZHANG Wei, YANG Zhengwei, et al. Fault Diagnosis for Turbo Pump of LRE by an Improved BP Algorithm[J]. Control Engineering of China, 2007 (S2): 144-146. [16] Shao S Y, MCALEER S, Yan R Q, et al. Highly accurate machine fault diagnosis using deep transfer learning[J]. IEEE Transactions on Industrial Informatics. 2019, 15 (4): 2446-2455. [17] SUN W, YANG G A, CHEN Q, et al. Fault diagnosis of rolling bearing based on wavelet transform and envelope spectrum correlation[J]. Journal of Vibration and Control, 2013, 19 (6): 924-941. [18] KUMAR A, GANDHI C P, VASHISHTHA G. VMD based trigonometric entropy measure: a simple and effective tool for dynamic degradation monitoring of rolling element bearing[J]. Measurement Science and Technology, 2022, 33: 014005. [19] GAO Q, DUAN C, FAN H, et al. Rotating machine fault diagnosis using empirical mode decomposition[J]. Mechanical Systems and Signal Processing, 2008, 22 (5): 1072-1081. [20] 潘海洋, 蒋婉婉, 郑近德, 等. ANSMD方法及其在齿轮故障诊断中的应用[J]. 振动与冲击, 2021, 40 (21): 113-119. PAN Haiyang, JIANG Wanwan, ZHENG Jinde, et al. ANSMD method and is application in gear fault diagnosis[J]. Journal of Vibration and Shock, 2021, 40 (21): 113-119. [21] WANG L, XIANG J W, LIU Y. Time-frequency-based maximum correlated kurtosis deconvolution approach for detecting bearing faults under variable speed conditions[J]. Measurement Science and Technology, 2019, 30 (12): 1361-6501. [22] HE W P, HU J, CHEN B Q, et al. GMC sparse enhancement diagnostic method based on the tunable Q-factor wavelet transform for detecting faults in rotating machines[J]. Measurement, 2022, 174: 109001. [23] ZHAO D F, LIU S L, ZHANG T, et al. Subdomain adaptation capsule network for unsupervised mechanical fault diagnosis[J]. Information Sciences, 2022, 611: 301-316. [24] ZHAO D F, LIU S L, DU H Y, et al. Deep branch attention network and extreme multi-scale entropy based single vibration signal-driven variable speed fault diagnosis scheme for rolling bearing[J]. Advanced Engineering Informatics, 2023,55:101844. [25] SHI Z, CHEN J, ZI Y, et al. A novel multitask adversarial network via redundant lifting for multi component intelligent fault detection under sharp speed variation[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 3511010. [26] LIU S, CHEN J, HE S, et al. Subspace network with shared representation learning for intelligent fault diagnosis of machine under speed transient conditions with few samples[J]. ISA Transactions, 2021, 128 (PtA): 531-544. [27] 孟宗, 关阳, 潘作舟, 等. 基于二次数据增强和深度卷积的滚动轴承故障诊断研究[J].机械工程学报, 2021,57 (23):106-115. MENG Zong, GUAN Yang, PAN Zuozhou, et al. Fault diagnosis of rolling bearing based on secondary data enhancement and deep convolutional network[J]. Journal of Mechanical Engineering, 2021,57 (23): 106-115. [28] 王骁贤, 陆思良, 何清波, 等. 变转速工况下基于多传感器信号深度特征融合的电机故障诊断研究[J].仪器仪表学报, 2022, 43 (3): 59-67. WANG Xiaoxian, LU Siliang, HE Qingbo, et al. Motor fault diagnosis based on deep feature fusion of multi-sensor data under variable speed condition[J]. Chinese Journal of Scientific Instrument, 2022, 43 (3): 59-67. [29] WU Z H, JIANG H K, LIU S W. A Gaussian-guided adversarial adaptation transfer network for rolling bearing fault diagnosis[J]. Advanced Engineering Informatics, 2022, 53: 101651. [30] DONOHO D L, GRIMES C. Locally linear embedding techniques for high-dimensional data[J]. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100 (10): 5591-5596. [31] TOLSTIKHIN I, HOULSBY N, KOLESNIKOV A, et al. MLP-Mixer: An all-MLP Architecture for Vision[EB/OL]. https://arxiv.org/abs/2105.01601, 2021.6.11. [32] LEI Y G, HE Z J, ZI Y Y, et al. Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs[J]. Mechanical Systems and Signal Processing, 2007, 21 (5): 2280-2294. [33] MAATEN L, HINTON G E. Visualizing high-dimensional data using t-SNE[J]. Machine Learning Research, 2008, 9: 2579-2605.

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