IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v243y2024ics0951832023007512.html

Dynamic model-assisted transferable network for liquid rocket engine fault diagnosis using limited fault samples

Author

Listed:
  • Wang, Chenxi
  • Zhang, Yuxiang
  • Zhao, Zhibin
  • Chen, Xuefeng
  • Hu, Jiawei

Abstract

The accurate detection and diagnosis of faults in Liquid Rocket Engines (LREs) are critical for ensuring space mission safety. However, the limited availability of actual fault samples and the diversification of potential faults present significant challenges in achieving precise diagnosis. To overcome these obstacles, we propose a dynamic model-assisted transfer learning approach. In this study, we first modularize the LRE and establish a dynamic model based on the mathematical principles of each module. Subsequently, we artificially established a fault module model, injected faults into the normal model, and simulated various fault modes to expand the fault sample library. Leveraging this augmented dataset in combination with a limited number of actual fault samples, we employ transfer learning to fine-tune a Convolutional Neural Network (CNN). Compared with other classic methods, the migrated CNN effectively adapts to the distribution of real data and significantly improves the accuracy of LRE fault diagnosis.

Suggested Citation

  • Wang, Chenxi & Zhang, Yuxiang & Zhao, Zhibin & Chen, Xuefeng & Hu, Jiawei, 2024. "Dynamic model-assisted transferable network for liquid rocket engine fault diagnosis using limited fault samples," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007512
    DOI: 10.1016/j.ress.2023.109837
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023007512
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109837?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Pan, Tongyang & Chen, Jinglong & Ye, Zhisheng & Li, Aimin, 2022. "A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    2. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Liu, Fuxiu & Li, Zhaojun & Liang, Minglang & Zhao, Binjian & Ding, Jiang, 2023. "Prediction method of non-stationary random vibration fatigue reliability of turbine runner blade based on transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. Li, Fudong & Chen, Jinglong & Liu, Zijun & Lv, Haixin & Wang, Jun & Yuan, Junshe & Xiao, Wenrong, 2022. "A soft-target difference scaling network via relational knowledge distillation for fault detection of liquid rocket engine under multi-source trouble-free samples," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Jiaxin & Rangaiah, Gade Pandu & Dong, Lichun & Samavedham, Lakshminarayanan, 2025. "An improved industrial fault diagnosis model by integrating enhanced variational mode decomposition with sparse process monitoring method," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    2. Wang, Shi-ao & Wang, Shibin & Wang, Wei & Zheng, Yiming & Ding, Baoqing & Yan, Ruqiang & Chen, Xuefeng, 2025. "Unrolled sparse coding via lifting wavelet for interpretable intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    3. Wang, Zhenya & Zhang, Meng & Chen, Hui & Li, Jinghu & Li, Gaosong & Zhao, Jingshan & Yao, Ligang & Zhang, Jun & Chu, Fulei, 2025. "A generalized fault diagnosis framework for rotating machinery based on phase entropy," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    4. Wang, Xin & Jiang, Hongkai & Mu, Mingzhe & Dong, Yutong, 2025. "A dynamic collaborative adversarial domain adaptation network for unsupervised rotating machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    5. Liao, Zengbu & Zhan, Keyi & Zhao, Hang & Deng, Yuntao & Geng, Jia & Chen, Xuefeng & Song, Zhiping, 2024. "Addressing class-imbalanced learning in real-time aero-engine gas-path fault diagnosis via feature filtering and mapping," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    6. Zhao, Shuaiyu & Duan, Yiling & Roy, Nitin & Zhang, Bin, 2024. "A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    7. Xiao, Xingyu & Qi, Ben & Liu, Shunshun & Chen, Peng & Liang, Jingang & Tong, Jiejuan & Wang, Haitao, 2025. "A dynamic risk-informed framework for emergency human error prevention in high-risk industries: A Nuclear Power Plant case study," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    8. Gu, Jiawei & Yuan, Xiangxiang & Li, Xinming & Wang, Yanxue & Xing, Jinduo, 2026. "Symmetric Radial Vectors for uncertainty-aware rotary machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 266(PA).
    9. Zhang, Xiao & Huang, Weiguo & Huangfu, Yifan & Wang, Jun & Ding, Chuancang & Du, Guifu, 2026. "EnergyMatch: A long-tailed semi-supervised learning framework for cross-domain fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 269(C).
    10. Guo, Chang & Shang, Zuogang & Ren, Jiaxin & Zhao, Zhibin & Ding, Baoqing & Wang, Shibin & Chen, Xuefeng, 2024. "CIS2N: Causal independence and sparse shift network for rotating machinery fault diagnosis in unseen domains," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    11. Sanchez, Luciano & Costa, Nahuel & Couso, Ines, 2025. "Addressing data scarcity in industrial reliability assessment with Physically Informed Echo State Networks," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    12. Liu, Shen & Chen, Jinglong & Liu, Zijun & Wang, Jun & Wang, Z. Jane, 2025. "Graph embedded patch-sense autoencoder with prior knowledge for multi-component system anomaly detection," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    13. Guo, Yu & Li, Xiangyu & Zhang, Jundong & Cheng, Ziyi, 2025. "SDCGAN: A CycleGAN-based single-domain generalization method for mechanical fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 258(C).
    14. Wang, Lin & Guo, Wannian & Guo, Junyu & Zheng, Shaocong & Wang, Zhiyuan & Kang, Hooi Siang & Li, He, 2025. "An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features," Reliability Engineering and System Safety, Elsevier, vol. 255(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Weicheng & Chen, Jinglong & Zhang, Tianci & Liu, Zijun & Wang, Jun & Zhang, Xinwei & He, Shuilong, 2023. "An asymmetrical graph Siamese network for one-classanomaly detection of engine equipment with multi-source fusion," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Wang, Zhe & Yang, Lechang & Fang, Xiaolei & Zhang, Hanxiao & Xie, Min, 2025. "Image-based remaining useful life prediction through adaptation from simulation to experimental domain," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    3. Wang, Hui & Wang, Shuhui & Yang, Ronggang & Xiang, Jiawei, 2024. "An optimized dynamic model improved deep discriminative transfer learning network for fault detection in rotation vector reducers," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    4. Liu, Yulang & Chen, Jinglong & Wang, Tiantian & Li, Aimin & Pan, Tongyang, 2023. "A variational transformer for predicting turbopump bearing condition under diverse degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    5. Li, Yuanfu & Chen, Yifan & Shao, Haonan & Zhang, Huisheng, 2023. "A novel dual attention mechanism combined with knowledge for remaining useful life prediction based on gated recurrent units," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    6. Zhang, Yadong & Wang, Shaoping & Zhang, Chao & Dui, Hongyan & Chen, Rentong, 2025. "Application of physics-informed machine learning in performance degradation and RUL prediction of hydraulic piston pumps," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    7. Dong, Jie & Li, Daye & Cong, Zhiyu & Peng, Kaixiang, 2025. "A new fault detection method based on an updatable hybrid model for hard-to-detect faults in nonstationary processes," Reliability Engineering and System Safety, Elsevier, vol. 259(C).
    8. Liu, Jiale & Wang, Huan, 2024. "A brain-inspired energy-efficient Wide Spiking Residual Attention Framework for intelligent fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Wen, Guangrui & Liu, Zimin & Su, Yu & Chen, Xuefeng, 2023. "Hybrid system response model for condition monitoring of bearings under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    10. Tan, Hongchuang & Xie, Suchao & Ma, Wen & Yang, Chengxing & Zheng, Shiwei, 2023. "Correlation feature distribution matching for fault diagnosis of machines," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    11. Shi, Yaowei & Deng, Aidong & Deng, Minqiang & Xu, Meng & Liu, Yang & Ding, Xue & Li, Jing, 2022. "Transferable adaptive channel attention module for unsupervised cross-domain fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    12. Mo, Renpeng & Zhou, Han & Yin, Hongpeng & Si, Xiaosheng, 2025. "A survey on few-shot learning for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 257(PB).
    13. Chang, Yuanhong & Li, Fudong & Chen, Jinglong & Liu, Yulang & Li, Zipeng, 2022. "Efficient temporal flow Transformer accompanied with multi-head probsparse self-attention mechanism for remaining useful life prognostics," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    14. Dong, Yutong & Jiang, Hongkai & Wu, Zhenghong & Yang, Qiao & Liu, Yunpeng, 2023. "Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    15. Guo, Xinxing & Huang, Song & Wu, Jianguo & Wang, Chao, 2025. "A transfer learning approach for remaining useful life prediction subject to hard failure considering within and between population variations," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    16. Dai, Menghang & Liu, Zhiliang & Wang, Jinrui & Zuo, Mingjian, 2024. "Physics-driven feature alignment combined with dynamic distribution adaptation for three-cylinder drilling pump cross-speed fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    17. Liu, Ruonan & Xie, Yunfei & Lin, Di & Zhang, Weidong & Ding, Steven X., 2024. "Information-based Gradient enhanced Causal Learning Graph Neural Network for fault diagnosis of complex industrial processes," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    18. Wang, Bo & Jiang, Ping & Guo, Bo, 2024. "A Bayesian design method for monopropellant engine system reliability qualification test plan," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    19. Yang, Jiayang & Zhao, Chunhui, 2025. "Addressing the label dilemma: A self-semi-supervised step-wise complementary label boosting strategy for industrial anomaly detection," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).
    20. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007512. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.