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    <identifier>10.57760/sciencedb.04211</identifier>
    <datestamp>2022-10-18T17:46:00Z</datestamp>
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  <dc:date>2022-10-18</dc:date>
  <dc:title>Application of dynamic fault tree in reliability assessment of reactor protection system</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.04211</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>[Background] Reactor protection system (RPS) has complex structure, numerous equipment, and continuous failure behaviors. The traditional static fault tree cannot accurately simulate the sequential failure behaviors of the RPS. [Purpose] This study aims to obtain the failure probability of related system of RPS and the sensitivity of each component by simulating the sequential failure behaviors of RPS, and provide suggestions for the optimal design and optimization of RPS in the future. [Methods] First of all, the dynamic fault tree (DFT) modeling tools was employed to model the reliability of the RPS, a dynamic binary tree method was applied to quantitative analysis of the DFT model. Then, the Latin hypercube sampling method was adopted to quantitatively analyze the uncertainty of the RPS failure probability, and compared with the traditional static fault tree method under the same conditions. [Results] Simulation results show that the failure probability with 0.95 confidence interval of RPS is 0.015 306~0.015 721. The two most sensitive components are the undervoltage drive card and the undervoltage relay. [Conclusions] DFT can accurately simulate the reliability of RPS, and provide more accurate results, which is beneficial for optimization design in the future.</dc:description>
  <dc:subject>Dynamic fault tree; Dynamic binary tree; Reliability; Latin hypercube sampling; Nuclear power plant</dc:subject>
  <dc:creator>Xueli Li</dc:creator>
  <dc:creator>Daochuan Ge</dc:creator>
  <dc:creator>Zhixian Lin</dc:creator>
  <dc:creator>Shaoxuan Wang</dc:creator>
  <dc:creator>Jianye Wang</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.11889/j.0253-3219.2021.hjs.44.100605</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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