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Airline safety assessment based on fuzzy mathematics and Bayesian networks

机译:基于模糊数学和贝叶斯网络的航空公司安全评估

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摘要

To reduce the airline accident risk probability, this paper proposes a new airline safety assessment method based on fuzzy mathematics and Bayesian networks (BN). Herein, we construct a safety assessment system encompassing five aspects-namely maintenance quality, aircraft technical state, environmental effects, emergency rescue, and safety management-and establish a BN model based on this safety assessment system. The fuzzy mathematical and statistical analyses are used to obtain the prior probabilities, and the data training function in GeNIe2.1 is used to obtain the conditional probabilities. Finally, we apply our method to an unspecified airline. The results indicate that the risk probability was 0.826 for the airline to have an excellent safety status in January 2018; this value was 0.886 according to fault tree analysis (FTA). In addition, by using vertical and horizontal analyses, we investigate the factors affecting airline safety. Thus, our BN-based method is more efficient than FTA because compared with FTA, the BN has the advantages, such as polymorphism and accuracy, particularly in detecting the most risky factors in a complex model.
机译:为了减少航空公司事故风险概率,本文提出了一种基于模糊数学和贝叶斯网络(BN)的新航空安全评估方法。在此,我们构建了一种安全评估系统,包括五个方面 - 即维护质量,飞机技术状态,环境影响,应急救援和安全管理 - 并根据本安全评估系统建立BN模型。模糊数学和统计分析用于获得现有概率,并且Genie2.1中的数据训练功能用于获得条件概率。最后,我们将我们的方法应用于未指定的航空公司。结果表明,航空公司的风险概率为0.826,以便2018年1月在良好的安全状况下具有优秀的安全状况;根据故障树分析(FTA),该值为0.886。此外,通过使用垂直和水平分析,我们研究影响航空公司安全的因素。因此,基于BN的方法比FTA更有效,因为与FTA相比,BN具有多态性和精度等优点,特别是在检测复杂模型中的最大风险因素。

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