基于SOM和关联规则的民机运行风险

熊明兰; 王华伟; 倪晓梅; 蔺瑞管

doi:10.13700/j.bh.1001-5965.2021.0102

基于SOM和关联规则的民机运行风险

doi: 10.13700/j.bh.1001-5965.2021.0102

南京航空航天大学民航学院, 南京 211106

基金项目:

国家自然科学基金 U1833110

详细信息

通讯作者:
王华伟, E-mail: wang_hw66@163.com

中图分类号: V328.2
计量
- 文章访问数: 259
- HTML全文浏览量: 69
- PDF下载量: 14
- 被引次数: 0
出版历程
- 收稿日期: 2021-03-02
- 录用日期: 2021-07-16
- 网络出版日期: 2021-07-29
- 整期出版日期: 2022-11-20

Operation risk of civil aircraft based on SOM and association rules

School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Funds:

National Natural Science Foundation of China U1833110

More Information

Corresponding author: WANG Huawei, E-mail: wang_hw66@163.com

摘要

摘要:
为充分认知民机风险, 实现从事故中学习, 以重大民机事故(MCAA)为研究对象挖掘出事故深层次的致因特征。针对MCAA信息具有可读性差, 系统行为具有非线性导致的无法直接获取运行风险信息, 难以直接建立事故致因的关联与映射关系, 提出一种从MCAA中学习民机运行风险特征的方法。针对民机运行特点, 结合事故信息及认知可靠性和失误分析方法(CREAM), 设计出MCAA-CREAM模型, 并构建民机多属性技术重大事故数据集。采用自组织映射(SOM)模型, 完成对事故的聚类分析和抽象特征映射, 以2D地图形式增强风险因素可读性, 利用关联规则有效挖掘风险因素间的强关联关系。
- 民机安全 /
- 风险特征 /
- 事故分析 /
- 自组织映射模型 /
- 关联规则
Abstract:
To fully understand the risks of civil aircraft and learning from accidents, the major civil aircraft accidents (MCAA) are used as the research object to dig out its deep-level causal characteristics. Due to the poor readability information and nonlinear system behavior of MCAA, it is difficult to directly obtain the operational risk information or establish association with mapping relationship of accident factors, a method of learning the operational risk characteristics of civil aircraft from major accidents is proposed. According to the operation characteristics of civil aircraft, and drawing on MCAA information and cognitive reliability, and error analysis method (CREAM), the MCAA-CREAM model is designed. Furthermore, the civil aircraft multi-attribute technology major accident dataset was constructed. To complete the cluster analysis and abstract feature mapping, we take the dataset as a sample, input it into the self-organizing maps (SOM) model, and enhance the readability of risk factors in the form of a 2D map. The strong association between risk factors can be mined by association rules.
- civil aircraft safety /
- risk characteristics /
- accident analysis /
- self-organizing maps model /
- association rules

HTML全文

图 1 MCAA-CREAM模型

Figure 1. MCAA-CREAM model

下载: 全尺寸图片幻灯片

图 2 基于SOM和关联规则的民机运行风险研究算法流程

Figure 2. Operational risk algorithm of civil aircraft based on SOM and AR

下载: 全尺寸图片幻灯片

图 3 2D聚类地图

Figure 3. 2D cluster map

下载: 全尺寸图片幻灯片

图 4 聚类质量指标

Figure 4. Cluster quality indicator

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图 5 MCAA中8个风险因素可视化地图

Figure 5. Visual maps of 8 risk factors in MCAA

下载: 全尺寸图片幻灯片

图 6 事故聚类群强关联规则可视化

Figure 6. Visualization of AR in accident clustering group

下载: 全尺寸图片幻灯片

表 1 聚类特征

Table 1. Clusters features

聚类族编号	包含案例数/个	频率/%	风险因素
聚类族编号	包含案例数/个	频率/%	最小值/个	最大值/个	平均值/个	中位数	众数
C1	111	46.06	1	12	5.16	5	4
C2	93	38.59	1	12	3.67	3	2
C3	37	15.35	3	15	8.5	8	7, 8

下载: 导出CSV

表 2 风险特征聚类统计结果

Table 2. Characteristic clustering statistical results of each risk factor

风险类别	风险子类别	C1 频率/%	C2 频率/%	C3 频率/%
设备	设备故障	18.90	40.09	27.00
	设计错误	0	12.90	0
	设计缺陷	15.30	39.80	21.60
程序	设计复杂	0.90	12.90	2.70
突发状况	信息模糊	14.40	6.50	8.10
突发状况	信息不完整	5.40	4.30	0
永久状况	信息错误	8.10	5.40	0
行为	执行时间错误	25.20	3.20	16.20
	用错执行设备	19.80	3.20	24.30
	执行顺序错误	17.10	2.20	10.80
	执行目标错误	9.90	0	8.10
	没有按手册执行	27.00	4.30	48.60
观察	没看到	11.70	2.20	16.20
观察	看错	4.50	1.10	5.40
计划	计划不充分	10.80	1.10	21.60
计划	优先级错误	10.80	1.10	21.60
解释	延迟措施	15.30	2.20	18.90
	错误预测	17.10	2.20	13.50
	决策错误	29.70	11.80	35.10
突发状况人的反应	记忆失效	5.40	0	10.80
	分心/粗心	28.80	1.10	40.50
	心理/精神压力	10.80	0	43.20
	疲劳	11.70	3.20	27.00
永久与机组相关的功能	身体疾病	0	0	16.20
	认知方式	11.70	8.60	56.80
	技能/知识不足	32.40	9.70	40.50
交流	交流障碍	5.40	1.10	18.90
交流	信息传递错误	24.30	5.40	2.80
组织	维修失效	1.80	19.40	8.10
	质量控制不足	9.90	35.50	10.80
	管理问题	35.10	38.70	73.00
	任务分配不足	0.90	0	62.20
	设计失效	2.70	21.50	13.50
培训	培训不足	3.60	5.40	18.90
周围环境条件	不利天气/地形	60.40	7.50	37.80
	夜间	6.30	6.50	10.80
	货物摆放/没油	0.90	9.70	0
	劫机/鸟击/军机击毁等意外	0	31.20	0
工作条件	工作场所布局不合理	0.90	4.30	2.70
工作条件	团队支持不足	7.20	2.20	67.60

下载: 导出CSV

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