北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (3): 624-633.doi: 10.13700/j.bh.1001-5965.2019.0268

• 论文 • 上一篇    下一篇

强电磁脉冲下柴油发动机系统薄弱环节识别

刘恬诗1, 赵昱1, 祝挺2, 孙铁刚1, 武艺1, 孙晓颖1   

  1. 1. 吉林大学 通信工程学院, 长春 130022;
    2. 东风特种装备事业部, 武汉 430058
  • 收稿日期:2019-05-31 发布日期:2020-03-28
  • 通讯作者: 赵昱 E-mail:yzhao@jlu.edu.cn
  • 作者简介:刘恬诗,女,硕士研究生。主要研究方向:汽车电磁兼容;赵昱,女,博士,副教授。主要研究方向:汽车电磁兼容。
  • 基金资助:
    装备预先研究项目(30105030302)

Weak links identification of diesel engine system under strong electromagnetic pulse

LIU Tianshi1, ZHAO Yu1, ZHU Ting2, SUN Tiegang1, WU Yi1, SUN Xiaoying1   

  1. 1. College of Communication Engineering, Jilin University, Changchun 130022, China;
    2. Dongfeng Special Equipment Division, Wuhan 430058, China
  • Received:2019-05-31 Published:2020-03-28
  • Supported by:
    Equipment Pre-research Project (30105030302)

摘要: 为识别强电磁脉冲环境下柴油发动机系统的薄弱环节,提出了一种加权故障树和分层贝叶斯网络相结合的柴油发动机系统薄弱环节识别方法。该方法综合考虑同层单元失效的相关性,加权故障树的局部应用解决了部分条件转移概率表不易获取问题。运用贝叶斯网络双向推理功能,首先,通过柴油发动机辐照试验和电磁仿真软件获得的各部件敏感度阈值及电磁应力数据,计算出强电磁脉冲下部件级到系统级的先验失效概率;然后,依据贝叶斯概率公式计算在发动机失效条件下各部件故障的后验概率,并排序以识别其薄弱环节,为电磁防护方案的设计提供参考和建议。以宽带高功率微波(WBHPM)辐照为例,说明了柴油发动机系统分层贝叶斯网络故障模型参数获取与概率计算过程。结果表明:执行器和凸轮轴位置传感器、曲轴位置传感器既为柴油发动机系统的重要部件,也为较薄弱环节,是需要重点防护的对象。

关键词: 分层贝叶斯网络, 薄弱环节识别, 后验概率, 先验失效概率, 柴油发动机系统

Abstract: To identify weaknesses in diesel engine systems under strong electromagnetic pulse,a method to study the identification of weak links in diesel engine system is proposed by combined weighted fault tree and hierarchical Bayesian network. This method takes into account the correlation of the failure of the same layer element, and the local application of the weighted fault tree solves the problem of obtaining partial conditional transfer probability tables. First, based on Bayesian network two-way reasoning function, the sensitivity threshold and electromagnetic stress data of each components were obtained by the diesel engine irradiation test and electromagnetic simulation software, the prior failure probability of component to the system level is calculated under strong electromagnetic pulse. Then, Bayesian probability formula is used to calculate the posterior probability of the components' failure under the condition of engine failure. The weak links of diesel engine system are identified according to the sequence of the components posterior probability, which may provide a reference for the design of electromagnetic protection scheme. Taking wide band high-power microwave (WBHPM) illumination as an example, the parameter acquisition and probability calculation process of the hierarchical Bayesian network fault model for diesel engines are illustrated. The results show that the actuator, camshaft and crankshaft sensors are not only important parts of diesel engine system, that but also is weak links, which need to be protected.

Key words: hierarchical Bayesian network, weak links identification, posterior probability, prior failure probability, diesel engine system

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