| Citation: | XIONG Minglan, WANG Huawei, NI Xiaomei, et al. Operation risk of civil aircraft based on SOM and association rules[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(11): 2325-2334. doi: 10.13700/j.bh.1001-5965.2021.0102(in Chinese)
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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.
| [1] |
张凡, 魏法杰, 李权葆. 复杂装备研制项目的风险源识别[J]. 北京航空航天大学学报, 2017, 43(5): 975-980. doi: 10.13700/j.bh.1001-5965.2016.0368
ZHANG F, WEI F J, LI Q B. Risk source identification of complex equipment development project[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(5): 975-980(in Chinese). doi: 10.13700/j.bh.1001-5965.2016.0368
|
| [2] |
IMAI S, BLASCH E, GALLI A, et al. Airplane flight safety using error-tolerant data stream processing[J]. IEEE Aerospace and Electronic Systems Magazine, 2017, 32(4): 4-17. doi: 10.1109/MAES.2017.150242
|
| [3] |
KELLY D, EFTHYMIOU M. An analysis of human factors in fifty controlled flight into terrain aviation accidents from 2007 to 2017[J]. Journal of Safety Research, 2019, 69: 155-165. doi: 10.1016/j.jsr.2019.03.009
|
| [4] |
PAN Y, ZHANG L M, WU X G, et al. Multi-classifier information fusion in risk analysis[J]. Information Fusion, 2020, 60: 121-136. doi: 10.1016/j.inffus.2020.02.003
|
| [5] |
CHEN Z Y, HUANG X P, YU S, et al. Risk analysis for clustered check dams due to heavy rainfall[J]. International Journal of Sediment Research, 2021, 36(2): 291-305. doi: 10.1016/j.ijsrc.2020.06.001
|
| [6] |
董雷霆, 周轩, 赵福斌, 等. 飞机结构数字孪生关键建模仿真技术[J]. 航空学报, 2021, 42(3): 113-141. https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202103012.htm
DONG L T, ZHOU X, ZHAO F B, et al. Key technologies for modeling and simulation of airframe digital twin[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(3): 113-141(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HKXB202103012.htm
|
| [7] |
GOERLANDT F, GOITEAB H, VALDEZ B O, et al. An analysis of wintertime navigational accidents in the Northern Baltic Sea[J]. Safety Science, 2017, 92: 66-84. doi: 10.1016/j.ssci.2016.09.011
|
| [8] |
MOURA R, BEER M, PATELLI E, et al. Learning from major accidents: Graphical representation and analysis of multi-attribute events to enhance risk communication[J]. Safety Science, 2017, 99: 58-70. doi: 10.1016/j.ssci.2017.03.005
|
| [9] |
余冠华. 基于多属性铁路事故数据集的聚类和关联规则分析方法研究[D]. 北京: 北京交通大学, 2019: 15-19.
YU G H. Research on clustering and association rule analysis method based on multi-attribute railway accident dataset[D]. Beijing: Beijing Jiaotong University, 2019: 15-19(in Chinese).
|
| [10] |
ZARANEZHAD A, MAHABADI H A, DEHGHANI R M. Development of prediction models for repair and maintenance-related accidents at oil refineries using artificial neural network, fuzzy system, genetic algorithm, and ant colony optimization algorithm[J]. Process Safety and Environmental Protection, 2019, 131: 331-348. doi: 10.1016/j.psep.2019.08.031
|
| [11] |
ASGARY A, ANSARI S, DUNCAN R, et al. Mapping potential airplane hazards and risks using airline traffic data[J]. International Journal of Disaster Risk Reduction, 2015, 13: 276-280. doi: 10.1016/j.ijdrr.2015.07.002
|
| [12] |
李哲, 徐浩军, 薛源, 等. 基于风险预测的飞行安全操纵空间构建方法[J]. 北京航空航天大学学报, 2018, 44(9): 1839-1849. doi: 10.13700/j.bh.1001-5965.2017.0686#viewType=Abstract
LI Z, XU H J, XUE Y, et al. Construction method of flight safety manipulation space based on risk prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1839-1849(in Chinese). doi: 10.13700/j.bh.1001-5965.2017.0686#viewType=Abstract
|
| [13] |
ZHANG Z H, HE Q, GAO J, et al. A deep learning approach for detecting traffic accidents from social media data[J]. Transportation Research Part C, 2018, 86: 580-596. doi: 10.1016/j.trc.2017.11.027
|
| [14] |
HOLLNAGEL E. Cognitive reliability and error analysis method (CREAM)[M]. Oxford: Elsevier Science Ltd., 1998: 151-190.
|
| [15] |
HU R J, RATNER K, RATNER E, et al. ELM-SOM+: A continuous mapping for visualization[J]. Neurocomputing, 2019, 365: 147-156. doi: 10.1016/j.neucom.2019.06.093
|
| [16] |
AVSAR B, ALIABADI D E. Parallelized neural network system for solving euclidean traveling salesman problem[J]. Applied Soft Computing, 2015, 34: 862-873. doi: 10.1016/j.asoc.2015.06.011
|
| [17] |
MERLIN P, SORJAMAA A, MAILLET B, et al. X-SOM and L-SOM: A double classification approach for missing value imputation[J]. Neurocomputing, 2009, 73(7): 1103-1108.
|
| [18] |
COMBERTⅡ L, DEMICHELA M, BALDISSONE G. A combined approach for the analysis of large occupational accident databases to support accident-prevention decision making[J]. Safety Science, 2018, 106: 191-202. doi: 10.1016/j.ssci.2018.03.014
|
| [19] |
安璐. 基于自组织映射的期刊主题研究[D]. 武汉: 武汉大学, 2009: 26-29.
AN L. Studying journal subjects with self-organizing map[D]. Wuhan: Wuhan University, 2009: 26-29(in Chinese).
|
| [20] |
ZHOU Y, LI C S, DING L Y, et al. Combining association rules mining with complex networks to monitor coupled risks[J]. Reliability Engineering and System Safety, 2019, 186: 194-208. doi: 10.1016/j.ress.2019.02.013
|
| [21] |
TEW C, GIRAUD C C, TANNER K, et al. Behavior-based clustering and analysis of interestingness measures for association rule mining[J]. Data Mining and Knowledge Discovery, 2014, 28(4): 1004-1045.
|
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