[1]吴建军, 朱晓彬, 程玉强, 崔孟瑜. 液体火箭发动机智能健康监控技术研究进展[J]. 推进技术, 2022, 43(1):??7-19.
WU J J, ZHU X B, CHENG Y Y. Research progress of intelligent health monitoring technology for liqui-propellant rocket engines[J]. Journal of Propulsion Technology, 2022, 43(1): 7-19. (in Chinese).
[2]包为民. 可重复使用运载火箭技术发展综述[J]. 航空学报, 2023, 44(23): 629555.
BAO W M. A review of reusable launch vehicle technology development[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(23): 629555. (in Chinese).
[3] S KANSO, M S JHA, M GA, et al. Remaining useful life prediction with uncertainty quantification of liquid propulsion rocket engine combustion chamber[J]. IFAC PapersOnLine, 2022, 55(6): 96–101.
[4] 陈泽灏, 陈晖, 高玉闪, 张航. 基于模型的液体火箭发动机故障诊断技术回顾与展望[J]. 航空学报, 2023, 44(23): 84-104.
CHEN Z H, CHEN H, GAO Y S, et al. Review and prospect of model-based fault diagnosis technology for liquid rocket engines[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(23): 84-104.(in Chinese).
[5] A IANNETTI, J MARZAT, H PIET-LAHANIER, et al. Automatic tuning strategies for model-based diagnosis methods applied to a rocket engine demonstrator [C], Proc. Third Eur. Conf. Prognostics Health Management Soc. 2016, Bilbao, Spain.
[6] Y MARU, H MORI, T OGAI, Anomaly Detection Configured as a Combination of State Observer and Mahalanobis-Taguchi Method for a Rocket Engine[J]. Transactions of the Japan Society for aeronautical and space sciences, aerospace technology Japan, 2018, 16(2): 195-201.
[7] J CHA, S KO, SY Park, E JEONG. 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.
[8] LEE K, CHA J, KO S, et al. Fault detection and diagnosis algorithms for an open cycle liquid propellant rocket engine using the Kalman filter and fault factor methods [J]. Acta Astronautica, 2018(150):15-27.
[9] 许亮, 芦弘炜, 王闻浩, 薛薇. 基于无迹卡尔曼滤波的液体火箭发动机故障诊断[J]. 载人航天,?2024,30(4):516-525.
XU L, LU H W, WANG W H, XUE W. Fault diagnosis of liquid rocket engine based on unscented Kalman filter [J]. Manned Space Flight, 2024,30(4):516-525. (in Chinese).
[10] ZENG Y Z, SHAO X D, HU Q L et al. ?Fault?diagnosis of liquid rocket engine thrust chamber based on?improved augmented particle filter [C]. The 40th Chinese Control Conference (CCC)?Shanghai, China,?2021.?
[11] S KANSO, M S JHA, M GALEOTTA, et al. Remaining useful life prediction with uncertainty quantification of liquid propulsion rocket engine combustion chamber [J]. IFAC Papers On Line, 2022, 55(6):96–101.
[12] D C BROCK, B GRAD.?Expert?systems: commercializing artificial intelligence[J]. IEEE Annals of the History of Computing,?2022, 44?(1):5-7.
[13] CHEN M Q, QU R, FANG W G. Case-based reasoning system for fault diagnosis of aero-engines [J]. Expert Systems With Applications, 2022,202:117350
[14] 黄敏超, 张育林, 陈启智. 基于模糊规则集度量的液体火箭发动机故障诊断[J]. 推进技术,?1997,?18(5):5-8.
HUANG M C, ZHANG Y L, CHEN Q Z. Fault diagnosis of liquid rocket engine based on fuzzy measuring of fuzzy rules sets [J]. Journal of Propulsion Technology, 1997,?18(5):5-8. (in Chinese)
[15] 董周杰, 郭迎清. 基于综合模糊聚类算法的液体火箭发动机故障诊断[J]. 航空动力学报, ?2020,35(6):1326-1334.
DONG Z J, GUO Y Q. Fault diagnosis of liquid rocket engine based on comprehensive fuzzy clustering algorithm [J]. Journal of Aerospace Power, 2020,35(6):1326-1334. (in Chinese)
[16] 孙成志, 闫晓东. 基于神经网络和证据理论的火箭发动机故障诊断[J]. 宇航总体技术,?2020, 4(4)?: 20-30.
SUN Z C, YAN X D. Fault diagnosis of rocket engine based on neural network and evidence theory[J]. Astronautical Systems Engineering Technology, 4(4): 20-30. (in Chinese)?
[17]黄卫东, 王克昌. 基于深层知识规则的液体火箭发动机故障诊断[J]. 宇航学报,?1997, 18(4)?:61-65.
HUANG W D, WANG K C. Rocket engine fault diagnosis based on rules of deep knowledge representation [J]. Journal of Astronautics, 1997, 18(4)?:61-65. (in Chinese).
[18] 陈启智. 液体火箭发动机故障检测与诊断研究的若干进展[J]. 宇航学报, 2003, 24(1):1-11.
CHEN QZ. Advances in fault detection and diagnosis of liquid rocket engines [J]. Acta Astronautics, 2003, 24(1):1-11. (in Chinese).
[19]刘冰,?张育林. 奇偶空间法用于液体火箭发动机故障诊断[J]. 推进技术,?1999,?20(6):P6-10.
LIU B, ZHANG Y L. Parity space method for fault diagnosis of liquid rocket engine [J]. Journal of Propulsion Technology,1999,?20(6):P6-10. (in Chinese).
[20] 高鸣,?任海峰,?胡小平,?吴建军. 独立分量分析在液体火箭发动机故障诊断中的应用[J]. 导弹与航天运载技术,?2013,4:74-77.
GAO M, REN H F, HU X P, WU J J. Independent component analysis for fault diagnosis of the liquid rocket engine [J]. Missiles and Space Vehicles, 2013, 4: 74-77. (in Chinese)
[21] N GEBRAEEL, Y LEI, N LI, et al. Prognostics and remaining useful life prediction of machinery: advances, opportunities and challenges [J]. J Dyn Monitor Diagnost, 2023, 2: 1-12
[22] 文成林, 吕菲亚. 基于深度学习的故障诊断方法综述[J]. 电子与信息学报, 2020, 42(1): 234-248.
WEN C L, LIU F Y. Review on Deep Learning Based Fault Diagnosis[J]. Journal of electronics and information technology, 2020, 42(1): 234-248. (in Chinese).
[23] M M F, I LARKI, R ZAHEDI, et al. Machine Learning and Deep Learning in Energy Systems[J]. A Review Sustainability, 2022, 14: 4832.
[24] GAO Q, WU X, GUO J, et al. Machine-Learning-Based Intelligent Mechanical Fault Detection and Diagnosis of Wind Turbines[J]. Mathematical Problems in Engineering, 2021, 2021: 1-11.
[25] S OCHELLA, M SHAFIEE, F DH. Artificial intelligence in prognostics and health management of engineering systems. Engineering Applications of Artificial Intelligence, 2022, 108: 104552.
[26] Y GONG, Z CHEN. A sequential approach to feature selection in high-dimensional additive models[J]. Journal of Statistical Planning and Inference, 2021, 215: 289-298.
[27] 余萍, 曹洁. 深度学习在故障诊断与预测中的应用 [J]. 计算机工程与应用, 2020, 56(3): 1-18.
XU P, CAO J. Deep Learning Approach and Its Application in Fault Diagnosis and Prognosis[J]. Computer engineering and applications, 2020, 56(3): 1-18. (in Chinese).
[28] NIE, Y, CHENG, Y Q, WU J. Dynamic cloud back-propagation networks and its application in fault diagnostic for liquid-propellant rocket engines[J], Proceedings of the Institution of Mechanical?Engineers, Part G: Journal of Aerospace Engineering, 2018, 232(3): 583-594.
[29] N LI, W XUE, X GUO, et al. Fault Detection in Liquid-Propellant Rocket Engines Based on Improved PSO-BP Neural Network[J]. Journal of Software, 2019, 14(8): 380-387.
[30] XU L, ZHAO S, LI N, et al. Application of QGA-BP for fault detection of liquid rocket engines[J]. IEEE Trans Aerosp Electron Syst, 2019, 55(5): 2464-72.
[31] S Young PARK, J AHN. Deep neural network approach for fault detection and diagnosis during startup transient of liquid-propellant rocket engine[J]. Acta Astronautica, 2020, 177: 714-730.
[32] Y F ZHOU, BH ChANG, H ZOU, et al. Online visual monitoring method for liquid rocket engine nozzle welding based on a multi-task deep learning model[J]. Journal of Manufacturing Systems, 2023, 68: 1-11.
[33] Y FENG, Z LIU, J CHEN, et al. Make the Rocket Intelligent at IoT Edge: Stepwise GAN for Anomaly Detection of LREs with Multi-source Fusion[J]. IEEE Internet of Things Journal, 2021: 2327-4662.
[34] SEIJI T, MIKI H, DAIWA S, et al. Data-driven fault detection in a reusable rocket engine using bivariate time-series analysis[J]. Acta Astronautica, 2021, 179: 685-694.
[35] FENG, Z, XU, T, et al. Comparison of SOM and PCA-SOM in fault diagnosis of ground-testing bed[J]. Procedia Engineering, 2011, 15: 1271-1276.
[36] SOWMYA R, MAIA R, CHRISTIAN B. Semi-Supervised Machine Learning for Spacecraft Anomaly Detection & Diagnosis[C]. 2020 IEEE Aerospace Conference Big Sky, MT, USA, 2020.
[37] MARK S, NIKUNJ O, BRYAN M. Unsupervised anomaly detection for liquid-fueled rocket propulsion health monitoring[J]. Journal of Aerospace Computing, Information, and Communication, 2009, 6(7): 464-482.
[38] XIAOBIN ZHU, YUQIANG CHENG, JIANJUN WU, et al. Steady-state process fault detection for liquid rocket engines based on convolutional autoencoder and one-class support vector machine[J]. IEEE Access, 2020, 8: 3144-3158.
[39] F LI, J CHEN, Z LIU, et al. A soft-target difference scaling network via relational knowledge distillation for fault detection of liquid rocket engine under multi-source trouble-free samples[J]. Reliability Engineering and System Safety, 2022, 228: 10875.
[40] H LV , J CHEN, J WANG, et al. A supervised framework for recognition of liquid rocket engine health state under steady-state process without fault samples[J]. IEEE Trans Instrum Meas, 2021, 70: 3518610.
[41] N. AISWARYA, S SUJA, K S MONI. An efficient approach for the diagnosis of faults in turbo pump of liquid rocket engine by employing FFT and time-domain features[J]. Australian Journal of Mechanical Engineering, 2018, 116(3): 163-172.
[42] Y XU, Y ZHAO, W KE, et al. A multi-fault diagnosis method based on improved SMOTE for class-imbalanced data [J]. The Canadian Journal of Chemical Engineering, 2023, 101 (4 ):1986-2001.
[43] JIN Y, YANG J, YANG X, et al. Cross-domain bearing fault diagnosis method based on SMOTE and deep transfer learning under imbalanced data[J]. Measurement Science and Technology, 2023, 35(1): 015121.
[44] H X LV, J L CHEN, J WANG, et al. A supervised framework for recognition of liquid rocket engine health state under steady-state process without fault samples[J]. IEEE Trans Instrum Meas, 2021,70:3518610.
[45] H P ZHANG, L L HUANG, C Q WU, et al. An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset[J]. Comput Netw 2020, 177:1–10.
[46] X W YANG, X Y XU, Y R WANG, et al. The Fault diagnosis of a plunger pump based on the SMOTE + tomek link and dual-channel feature fusion [J]. Applied Sciences, 2024, 14 (11): P4785.
[47] S DAIWA, T SEIJI, H MIKI, et al. Estimating model parameters of liquid rocket engine simulator using data assimilation [J]. Acta Astronautica, 2020, 177:373-385.
[48] H MIKI, S DAIWA, T SEIJI, et al. Complementary-integrated approach to model-based and data-driven prognostics and fault diagnosis for Reusable Rocket Engine Systems [J]. Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, 2021, 19(2): 160-169.
[49] C X WANG, Y X ZHANG, Z B ZHAO, et al. Dynamic model-assisted transferable network for liquid rocket engine fault diagnosis using limited fault samples [J]. Reliability Engineering & System Safety, 2024, 243:109837.
[50] 程玉强,邓凌志. Wasserstein距离在液体火箭发动机故障检测中的应用[J].国防科技大学学报,?2023,?45(4):20-27.
Y Q CHENG, L Z DENG. Application of Wasserstein distance in fault detection for liquid-propellant rocket engines [J]. Journal of National University of Defense Technology, 2023,45 (4): :20-27. (in Chinese).
[51] L Z DENG, Y Q CHENG, S M YANG et al. Fault detection and diagnosis for liquid rocket engines with sample imbalance based on Wasserstein generative adversarial nets and multilayer perceptron [J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2022, 237 (8):1751-1763.
[52] D F ZHAO, S L LIU, Z H MIAO et al. Subdomain adaptation joint attention network enabled two-stage strategy towards few-shot fault diagnosis of LRE turbopump [J]. Advanced?Engineering?Informatics,?2024,?60:1474-0346.
[53] S YAN, X ZHONG, H D SHAO et al. Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization [J]. Reliability Engineering & System Safety, 2023, 239: 0951-8320.
[54] Y QIN, H Y LIU, Y F MAO. Faulty rolling bearing digital twin model and its application in fault diagnosis with imbalanced samples [J]. Advanced Engineering Informatics, 2024, 61: 1474-0346.
[55] X YU, H S YIN, L SUN. A new cross-domain bearing fault diagnosis framework based on transferable features and manifold embedded discriminative distribution adaption under class imbalance [J]. IEEE Sensors Journal, 2023, 23 (7): 7525-7545.
[56] M IRFAN, Z MUSHTAQ, N A KHAN et al. Improving bearing fault identification by using novel hybrid involution-convolution feature extraction with adversarial noise injection in conditional GANs [J]. IEEE Access, 2023, 11: 118253-118267.
[57] S LI, YF PENG, Y P SHEN et al. Rolling bearing fault diagnosis under data imbalance and variable speed based on adaptive clustering weighted oversampling [J]. Reliability Engineering & System Safety, 2024, 244: 109938.
[58] Z Z HAN, H R WANG, C SHEN et al. Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance [J]. Measurement Science and Technology, 2024, 35(3): 035002.
[59] Z Y XING, R Z ZHAO, Y C WU et al. Intelligent fault diagnosis of rolling bearing based on novel CNN model considering data imbalance [J]. Applied Intelligence, 2022, 52 (14): 16281-16293.
[60] H YU, W Z LIU, Y J GU et al. TEM-SDRF: A novel approach for machinery fault diagnosis amid dual imbalances in fault and noise samples [J]. IEEE Sensors Journal, 2024, 24 (14): 1.
[61] S R SAUFI, Z A BIN, M S LEONG, M H LIM. Gearbox fault diagnosis using a deep learning model with limited data sample [J]. IEEE Trans Ind Informatics, 2020, 16: 6263–71.
[62] F JIA, Y LEI, N LU, S XING. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mech Syst Signal Process, 2018, 110:349–67.
[63] S MALDONADO, J LOPEZ. Dealing with high-dimensional class imbalanced datasets: embedded feature selection for SVM classification [J]. Applied Soft Computing, 2018, 67:94-105.
[64] Q F XU, S X LU, W Y JIA, C X JIANG. Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning [J]. Journal of Intelligent Manufacturing, 2020, 31:1467-1481.
[65] F JIA, S LI, H ZUO, J SHEN. Deep neural network ensemble for the intelligent fault diagnosis of machines under imbalanced data [J]. IEEE Access, 2020, 8:120974–82.
[66] ZHUANG F, QI Z, DUAN, K, et al. A Comprehensive Survey on Transfer Learning[J]. Proc. IEEE, 2020, 109, 43-59.
[67] F YU, X XIU, Y Li, A Survey on Deep Transfer Learning and Beyond[J]. Mathematics, 2022, 10(3619): 3619.
[68] S TANG, J MA , Z YAN, et al. Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery[J]. Engineering Applications of Artiffcial Intelligence, 2024, 134: 108678.
[69] C ZHANG, S TANG, K TANG. Strategies of parameter fault detection for rocket engines based on transfer learning[J]. J Comput Appl, 2020,40: 2774.
[70] T PAN, J CHEN, Z YE, et al. A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines[J]. Reliability Engineering and System Safety 2022, 225: 108610.
[71] C WANG, Y ZHANG, Z ZHAO, et al. Dynamic model-assisted transferable network for liquid rocket engine fault diagnosis using limited fault samples [J] Reliability Engineering & System Safety, 2024, 243: 109837.
[72] ZHAO YP, JIN HJ, LIU H. Gas Path Fault Diagnosis of Turboshaft Engine Based on Novel Transfer Learning Methods[J]. Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, 2024, 146(3): 031010.
[73] J HU, M CHEN, H TANG, et al. An adversarial transfer learning method based on domain distribution prediction for aero-engine fault diagnosis[J]. Engineering Applications of Artificial Intelligence, 2024, 133: 108287.
[74] Y FENG, J CHEN, J XIE, et al. Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects[J]. Knowledge-Based Systems, 2022, 235: 107646.
[75] L WANG, D HUANG, K HUANG, et al. Online meta-learning approach for sensor fault diagnosis using limited data[J]. Smart Materials and Structures, 2024, 33(8): 085016.
[76] Z XU, X GAO, J FU, et al. A novel fault diagnosis method under limited samples based on an extreme learning machine and meta-learning[J]. Journal of the Taiwan Institute of Chemical Engineers, 2024, 161: 105522.
[77] J ZHAO, T TANG, Y YU, et al. Adaptive meta transfer learning with efficient self-attention for few-shot bearing fault diagnosis[J]. Neural Processing Letters, 2023, 55(2): 949-968.
[78] O VINYALS, C BLUNDELL, T LILLICRAP, et al. Matching networks for one shot learning[J]. Advances in neural information processing systems, 2016, 29.
[79] J ZHONG, K GU, H JIANG, et al. A fine-tuning prototypical network for few-shot cross-domain fault diagnosis[J]. Measurement Science and Technology, 2024, 35(11): 116124.
[80] R MA, T HAN, W LEI. Cross-domain meta learning fault diagnosis based on multi-scale dilated convolution and adaptive relation module[J]. Knowledge-Based Systems, 2023, 261: 110175.
[81] L K, KD, D E, et al. A comprehensive review on zero-shot-learning techniques [J]. Intelligent Decision Technologies 2024, 18(2): 1001-1028.
[82] S ZHANG, H Wei, J DING. An effective zero-shot learning approach for intelligent fault detection using 1D CNN[J]. Applied Intelligence, 2023, 53(12): 16041-16058.
[83] J XU, S LIANG, X DING, et al. A zero-shot fault semantics learning model for compound fault diagnosis[J]. Expert Systems with Applications, 2023, 221: 119642.
[84] LI C, LUO K, YANG L, et al. A zero-shot fault detection method for UAV sensors based on a novel CVAE-GAN model[J]. IEEE Sensors Journal, 2024 24(14):11530-437X
[85] ZHAO D, YIN H, ZHOU H. A Zero-Sample Fault Diagnosis Method Based on Transfer Learning[J]. IEEE Transactions on Industrial Informatics, 2024: P1-111551-3203.
[86] SERKAN K, OZER C D, AMIR A. Zero-shot motor health monitoring by blind domain transition[J]. Mechanical Systems and Signal Processing, 2024,210: 111147.
[87] S E ORTIGOSSA, T GONCALVES, L G NONATO. Explainable artificial intelligence (XAI)—from theory to methods and applications [J]. IEEE Access, 2024,12: P80799-80846.
[88] S MOOSAVI, M FARAJZADEH-ZANJANI, R RAZAVI-FAR, et al. Explainable ai in manufacturing and industrial cyber-physical systems: A survey[J]. Electronics, 2024, 13(17): 3497.
[89]蒲天骄,乔骥,赵紫璇,赵鹏. 面向电力系统智能分析的机器学习可解释性方法研究(一):基本概念与框架[J]. 中国电机工程学报,?2023,?43(18): 7010-7030.
T J PU, J QIAO, Z X ZHAO, P ZHAO. Research on interpretable methods of machine learning applied in intelligent analysis of power system (Part I): basic concept and framework [J]. Proceedings of the CSEE, 2023, 43(18): 7010-7030. (in Chinese).
[90] N HERWIG, P BORGHESANI. Explaining deep neural networks processing raw diagnostic signals[J]. Mechanical Systems and Signal Processing, 2023, 200: 110584.
[91] A AMIN, A BIBO, M PANYAM, et al. Wind turbine gearbox fault diagnosis using cyclostationary analysis and interpretable CNN [J]. Journal of Vibration Engineering & Technologies, 2024, 12(2): 1695-1705.
[92] A FERRARO, A GALLI, V MOSCATO, et al. Evaluating explainable artificial intelligence tools for hard disk drive predictive maintenance[J]. Artificial Intelligence Review, 2023, 56(7): 7279-7314.
[93] C UTAMA, C MESKE, J SCHNEIDER, et al. Explainable artificial intelligence for photovoltaic fault detection: A comparison of instruments[J]. Solar Energy, 2023, 249: 139-151.
[94] J F BARRAZA, E L DROGUETT, M R MARTINS. Fs-scf network: Neural network interpretability based on counterfactual generation and feature selection for fault diagnosis[J]. Expert Systems with Applications, 2024, 237: 121670.
[95] J OBREGON, J Y. JUNG Rule-based visualization of faulty process conditions in the die-casting manufacturing[J]. Journal of Intelligent Manufacturing, 2024, 35(2): 521-537.
[96] H C HU, C C LU, S W WANG, et al. Rule generation for classifying slt failed parts[C]//2022 IEEE 40th VLSI Test Symposium (VTS). IEEE, 2022: 1-7.
[97] J LIU, L HOU, R ZHANG, et al. Explainable fault diagnosis of oil-gas treatment station based on transfer learning[J]. Energy, 2023, 262: 125258.
[98] B HOOVER, H STROBELT, S GEHRMANN. Exbert: A visual analysis tool to explore learned representations in transformers models[A]. 2019.
[99] M SHAJALAL, A BODEN, G STEVENS. Forecastexplainer: Explainable household energy demand forecasting by approximating shapley values using deeplift[J]. Technological Forecasting and Social Change, 2024, 206: 123588.
[100] G LI, F LI, C XU, et al. A spatial-temporal layer-wise relevance propagation method for improving inter- pretability and prediction accuracy of lstm building energy prediction[J]. Energy and Buildings, 2022, 271: 112317.
[101]纪守领, 李进锋, 杜天宇,李博. 机器学习模型可解释性方法、应用与安全研究综述 [J]. 计算机研究与发展,?2019,? 56(10): 2071-2096.??
S L JI, J F LI, T Y DU, B LI. Survey on techniques, applications and security of machine learning interpretablity [J]. Journal of Computer Research and Development, ?2019,? 56(10): 2071-2096.?(in Chinese).
[102]陈珂锐, 孟小峰. 机器学习的可解释性[J]. 计算机研究与发展, 2020 , 57(9): 1971-1986.
CHEN H R, MENG X F. Interpretation and understanding in machine learning[J]. Journal of Computer Research and Development, 2020 , 57(9): 1971-1986. (in Chinese).