强电磁脉冲下柴油发动机系统薄弱环节识别

刘恬诗; 赵昱; 祝挺; 孙铁刚; 武艺; 孙晓颖

doi:10.13700/j.bh.1001-5965.2019.0268

强电磁脉冲下柴油发动机系统薄弱环节识别

doi: 10.13700/j.bh.1001-5965.2019.0268

刘恬诗¹,
赵昱^1, ,,
祝挺²,
孙铁刚¹,
武艺¹,
孙晓颖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

基金项目:

装备预先研究项目 30105030302

详细信息

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

通讯作者:
赵昱, E-mail: yzhao@jlu.edu.cn

中图分类号: O441.4
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出版历程
- 收稿日期: 2019-05-31
- 录用日期: 2019-09-20
- 网络出版日期: 2020-03-20

Weak links identification of diesel engine system under strong electromagnetic pulse

1.
College of Communication Engineering, Jilin University, Changchun 130022, China
2.
Dongfeng Special Equipment Division, Wuhan 430058, China

Funds:

Equipment Pre-research Project 30105030302

More Information

Corresponding author: ZHAO Yu, E-mail: yzhao@jlu.edu.cn

摘要

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。
- 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.
- hierarchical Bayesian network /
- weak links identification /
- posterior probability /
- prior failure probability /
- diesel engine system

HTML全文

图 1 柴油发动机电控结构原理图

Figure 1. Schematic diagram of electronic control structure of diesel engine

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图 2 柴油发动机薄弱环节识别流程图

Figure 2. Identification flowchart of weak links of diesel engine

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图 3 柴油发动机系统分层贝叶斯网络故障模型

Figure 3. Diesel engine system hierarchical Bayesian network fault model

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图 4 柴油发动机系统分层贝叶斯网络故障精简模型

Figure 4. Modified diesel engine system hierarchical Bayesian network fault model

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图 5 WBHPM辐照下的车辆线缆

Figure 5. Car cable illuminated by WBHPM

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图 6 WBHPM时、频域波形

Figure 6. Time and frequency domain waveforms for WBHPM

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图 7 CAN总线监控系统示意图

Figure 7. Schematic diagram of CAN bus monitoring system

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图 8 试验现场布置

Figure 8. Experimental site arrangement

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图 10 事件关系

Figure 10. Relationship between events

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表 1 部件敏感度阈值及失效概率

Table 1. Component sensitivity threshold and failure probability

部件	μ_b/V	σ_b/V	P_c	P_t	P(C)
曲轴位置传感器	149.55	2.18	0.7408	0.7719	0.5718
凸轮轴位置传感器	152.76	3.42	0.7261	0.7712	0.5600
油轨压力传感器	157.18	3.92	0.7025	0.7209	0.5064
加速踏板位置传感器	156.63	1.29	0.7092	0.7210	0.5113
冷却液温度传感器	193.01	6.61	0.5232	0.6290	0.3290
进气温度压力传感器	179.22	4.68	0.5969	0.6693	0.3995
大气压力传感器	170.59	4.41	0.6068	0.6931	0.4206
燃油计量阀	254.01	2.67	0.2211	0.2552	0.5025
喷油器	228.26	2.18	0.3713	0.3376	0.5427

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表 2 层次分析法1~9标度的含义

Table 2. Meaning of scale 1-9 in analytic hierarchy process

标度	因素i与因素j比值的含义
1	两者的重要性相同
3	i比j稍重要
5	i比j明显重要
7	i比j强烈重要
9	i比j极端重要
2，4，6，8	上述相邻比值重要性的中间值
倒数	i与j比值

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表 3 一致性检验对照

Table 3. Consistency check comparison

n	RI
1	0
2	0
3	0.515
4	0.893
5	1.119
6	1.249
7	1.345
8	1.42
9	1.462
10	1.487

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表 4 柴油发动机系统节点条件转移概率

Table 4. Nodal conditional transfer probability of diesel engine system

CPT	条件转移概率公式	概率值
CPT	条件转移概率公式	m=0	m=1
CPT1	P(V\|S₁=0，S₂=0)	1	0
CPT1	其他	0	1
CPT2	P(S₁=m\|C_1.1=0，C_1.3=0)	1	0
	P(S₁=m\|C_1.1=1，C_1.3=0)	0.2	0.8
	其他	0	1
CPT3	P(S₂=m\|S₁=0, C_2.1=0，C_2.3=0)	1	0
	P(S₂=m\|S₁=0, C_2.1=1，C_2.3=0)	0.2	0.8
	其他	0	1
CPT4/5	P(C_i.3=m\|C_i.1=0)	1	0
CPT4/5	P(C_i.3=m\|C_i.1=1)	0.2	0.8
注：柴油发动机各单元工作状态可分为正常和失效两种情况，m=0表示正常工作，m=1表示失效状态；CPT表示贝叶斯网络各节点条件转移概率。

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表 5 各部件后验概率

Table 5. Posterior probability of various components

部件	后验概率
曲轴位置传感器	P(C_1.1.1\|V)=0.5089
凸轮轴位置传感器	P(C_1.1.2\|V)=0.4984
油轨压力传感器	P(C_1.1.3\|V)=0.0845
燃油计量阀	P(C_1.3\|V)=0.6163
加速踏板位置传感器	P(C_2.1.3\|V)=0.1308
冷却液温度传感器	P(C_2.1.4\|V)=0.0280
进气温度压力传感器	P(C_2.1.5\|V)=0.0341
大气压力传感器	P(C_2.1.6\|V)=0.0154
喷油器	P(C_2.3\|V)=0.6656

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