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

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  • 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
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    References listed on IDEAS

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    2. 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).
    3. 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).
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    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).
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