IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v248y2024ics0951832024002473.html
   My bibliography  Save this article

A Bayesian design method for monopropellant engine system reliability qualification test plan

Author

Listed:
  • Wang, Bo
  • Jiang, Ping
  • Guo, Bo

Abstract

During the development of systems, traditional System Reliability Qualification Testing (SRQT) is typically utilized to assess whether they meet predefined reliability standards. However, this approach often demands substantial sample sizes and prolonged test durations, rendering it impractical for costly, highly reliable systems with limited sample sizes. Additionally, the extended test duration may not align with practical time-to-market pressures or budget constraints. To overcome these challenges, the study integrates subsystem data into the monopropellant engine system RQT plan's design. By leveraging Monte-Carlo simulation, subsystem data is modeled to create a system parameter distribution, enabling the formulation of SRQT plans based on posterior risks. An example of a monopropellant liquid rocket engine system is provided to demonstrate the advantages and applications of the proposed methodology.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:248:y:2024:i:c:s0951832024002473
    DOI: 10.1016/j.ress.2024.110173
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024002473
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110173?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 search 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. Guo, Jian & (Steven) Li, Zhaojun & (Judy) Jin, Jionghua, 2018. "System reliability assessment with multilevel information using the Bayesian melding method," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 146-158.
    3. Firouzi, Mohsen & Samimi, Abouzar & Salami, Abolfazl, 2022. "Reliability evaluation of a composite power system in the presence of renewable generations," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Zheng, Huiling & Yang, Jun & Zhao, Yu, 2023. "Reliability demonstration test plan for degraded products subject to Gamma process with unit heterogeneity," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    5. Jongseon Jeon & Suneung Ahn, 2018. "Bayesian Methods for Reliability Demonstration Test for Finite Population Using Lot and Sequential Sampling," Sustainability, MDPI, vol. 10(10), pages 1-11, October.
    6. Betz, Wolfgang & Papaioannou, Iason & Straub, Daniel, 2022. "Bayesian post-processing of Monte Carlo simulation in reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. 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).
    3. Salomon, Julian & Winnewisser, Niklas & Wei, Pengfei & Broggi, Matteo & Beer, Michael, 2021. "Efficient reliability analysis of complex systems in consideration of imprecision," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. 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).
    5. Huimin Wang & Zhaojun Steven Li, 2022. "An AdaBoost-based tree augmented naive Bayesian classifier for transient stability assessment of power systems," Journal of Risk and Reliability, , vol. 236(3), pages 495-507, June.
    6. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2023. "Determination of the Optimal Level of Reactive Power Compensation That Minimizes the Costs of Losses in Distribution Networks," Energies, MDPI, vol. 17(1), pages 1-24, December.
    7. 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).
    8. Xu, Yingchun & Yao, Wen & Zheng, Xiaohu & Chen, Xiaoqian, 2020. "An iterative information integration method for multi-level system reliability analysis based on Bayesian Melding Method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Stover, Oliver & Karve, Pranav & Mahadevan, Sankaran, 2023. "Reliability and risk metrics to assess operational adequacy and flexibility of power grids," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Wang, Chaonan & Wang, Shuli & Xing, Liudong & Guan, Quanlong, 2023. "Efficient performability analysis of dynamic multi-state k-out-of-n: G systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    11. Cheng, Yongbo & Qv, Junheng & Feng, Ke & Han, Te, 2024. "A Bayesian adversarial probsparse Transformer model for long-term remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    12. Siripha Junlakarn & Radhanon Diewvilai & Kulyos Audomvongseree, 2022. "Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation," Energies, MDPI, vol. 15(14), pages 1-27, July.
    13. Cao, Lixiao & Zhang, Hongyu & Meng, Zong & Wang, Xueping, 2023. "A parallel GRU with dual-stage attention mechanism model integrating uncertainty quantification for probabilistic RUL prediction of wind turbine bearings," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    14. Jain, Tanmay & Verma, Kusum, 2024. "Reliability based computational model for stochastic unit commitment of a bulk power system integrated with volatile wind power," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    15. Martón, I. & Sánchez, A.I. & Carlos, S. & Mullor, R. & Martorell, S., 2023. "Prognosis of wear-out effect on of safety equipment reliability for nuclear power plants long-term safe operation," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    16. Yingchun Xu & Xiaohu Zheng & Wen Yao & Ning Wang & Xiaoqian Chen, 2021. "A sequential multi-prior integration and updating method for complex multi-level system based on Bayesian melding method," Journal of Risk and Reliability, , vol. 235(5), pages 863-876, October.
    17. Meng, Yuan & Zhang, Dequan & Shi, Baojun & Wang, Dapeng & Wang, Fang, 2024. "An active learning Kriging model with approximating parallel strategy for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    18. Guo, Yongjin & Wang, Hongdong & Guo, Yu & Zhong, Mingjun & Li, Qing & Gao, Chao, 2022. "System operational reliability evaluation based on dynamic Bayesian network and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    19. Zou, Yanhua & ÄŒepin, Marko, 2024. "Loss of load probability for power systems based on renewable sources," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    20. He, Xinran & Ding, Tao & Zhang, Xiaosheng & Huang, Yuhan & Li, Li & Zhang, Qinglei & Li, Fangxing, 2023. "A robust reliability evaluation model with sequential acceleration method for power systems considering renewable energy temporal-spatial correlation," Applied Energy, Elsevier, vol. 340(C).

    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:248:y:2024:i:c:s0951832024002473. 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.