SDR data-driven safety status evaluation of civil aircraft

doi:10.16265/j.cnki.issn 1003-3033.2021.11.012

Abstract

Abstract: To identify safety status of civil aircraft, a safety status evaluation method based on SDR data was proposed. Firstly, SDR data of A320 models were analyzed, and an evaluation index system was constructed according to civil aircraft system/component code classification standards. Then, index weights were obtained by using criteria importance through intercriteria correlation (CRITIC) method. Finally, an evaluation model was constructed on the basis of index fuzzy segmentation and MULTIMOORA, and a case study was conducted by utilizing this model. The results show that the occurrence frequency of SDR generally obeys normal distribution, and classification standards of safety status can be determined. Moreover, advantage theory can comprehensively rank the results of ratio system, reference point method and full multiplicative form. Most of the safety status of A320 aircraft type are in the middle level, which is basically consistent with actual situation.

Key words: service difficulty report (SDR), civil aircraft, safety status, index fuzzy segmentation model, MOORA plus the full multiplicative form (MULTIMOORA)

CLC Number:

X949

YANG Xiaoqiang, LIN Xiangyi, JIANG Lan. SDR data-driven safety status evaluation of civil aircraft[J]. China Safety Science Journal, 2021, 31(11): 85-92.

References

[1] 解海涛. 基于遗传神经网络的民用航空器SDR预测[D]. 南京:南京航空航天大学, 2009.
XIE Haitao. Predicting of aircraft SDRs based on genetic-nerual network [D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2009.
[2] 韦艺, 陈新锋. 使用困难报告安全性风险评估方法研究[J]. 中国民航大学学报, 2011,29(2):42-46.
WEI Yi, CHEN Xinfeng. Research of safety risk evaluation for service difficulty report [J]. Journal of Civil Aviation University of China, 2011,29(2):42-46.
[3] 魏德才, 张诤敏, 李闯,等. 基于模糊贴近度和熵权的航空装备维修安全综合评价[J]. 中国安全科学学报,2012,22(2):107-111.
WEI Decai, ZHANG Zhengmin, LI Chuang, et al. Airline equipment maintenance safety comprehensive evaluation based on fuzzy asymmetric proximity and entropy [J]. China Safety Science Journal, 2012,22(2):107-111.
[4] 汪送, 王瑛, 李超. BP神经网络在航空机务人员本质安全程度评价中的应用[J]. 中国安全生产科学技术,2010,6(6):35-39.
WANG Song, WANG Ying, LI Chao. Application of BP neural network in inherent safety degree evaluation of aviation maintenance personnel [J]. Journal of Safety Science and Technology, 2010,6(6):35-39.
[5] 孙瑞山, 杨绎煊, 汪磊. QAR数据在飞行安全评价中的应用[J]. 中国安全科学学报, 2015,25(7):87-92.
SUN Ruishan, YANG Yixuan, WANG Lei. Study on flight safety evaluation based on QAR data [J]. China Safety Science Journal, 2015,25(7):87-92.
[6] MILAN J. An assessment of risk and safety in civil aviation [J]. Journal of Air Transport Management, 2000,6(1):43-50.
[7] 胡宗顺, 黄之杰, 朱倩,等. 基于BP和Hopfield神经网络的航空肼燃料保障安全评价[J]. 火力与指挥控制, 2017,42(11):121-125.
HU Zongshun, HUANG Zhijie, ZHU Qian, et al. Research of aerial hydrazine fuel safety assessment based on BP and Hopfield neural network [J]. Fire Control & Command Control, 2017,42(11):121-125.
[8] 曾亮. 多层次模糊评估法在民航不安全事件风险评估中的应用[J]. 中国安全科学学报, 2008,18(1):131-138.
ZENG Liang. Application of multi-layer fuzzy evaluation method to risk assessment in civil aviation [J]. China Safety Science Journal, 2008,18(1):131-138.
[9] 中国民用航空局. 民用航空器使用困难报告和调查[L].2011-12-19.
[10] 汪伟, 贾宝山, 祁云. 改进CRITIC修正G2-TOPSIS的钻孔自燃预测模型及应用[J]. 中国安全科学学报, 2019,29(11):26-31.
WANG Wei, JIA Baoshan, QI Yun. Prediction model of spontaneous combustion risk of extraction drilling based on improved CRITIC modified G2-TOPSIS method and its application [J]. China Safety Science Journal, 2019,29(11):26-31.
[11] 邹强, 周建中, 杨小玲,等. 属性区间识别模型在溃坝后果综合评价中的应用[J]. 四川大学学报:工程科学版, 2011,43(2):45-50.
ZOU Qiang, ZHOU Jianzhong, YANG Xiaoling, et al. A comprehensive assessment model for severity degree of dam failure impact based on attribute interval recognition theory [J]. Journal of Sichuan University:Engineering Science Edition, 2011,43(2):45-50.
[12] 李群, 宁利. 属性区间识别理论模型研究及其应用[J]. 数学的实践与认识, 2002,32(1):50-54.
LI Qun, NING Li. Attribute interval recognition theoretical model with application [J]. Mathematics in Practice and Theory, 2002,32(1):50-54.
[13] 蒋澜. 基于SDR数据的民用航空器安全评价与预测研究[D]. 广汉:中国民用航空飞行学院, 2019.
JIANG Lan. Study of civil aircraft safety evaluation and prediction based on SDR data [D]. Guanghan:Civil Aviation Flight University of China, 2019.
[14] 熊升华, 陈振颂, 陈勇刚,等. 基于指标模糊分割和MULTIMOORA的航空公司机队可靠性识别模型[J]. 计算机集成制造系统, 2019,25(2):431-438.
XIONG Shenghua, CHEN Zhensong, CHEN Yonggang, et al. Recognition method of airline fleet reliability based on index fuzzy segmentation and MULTIMOORA [J]. Computer Integrated Manufacturing Systems, 2019,25(2):431-438.
[15] 赵新斌, 王岩. 基于偏态分布的飞行品质风险度量方法[J]. 中国安全科学学报, 2019,29(10):160-166.
ZHAO Xinbin, WANG Yan. Risk measurement method of flight operational quality based on skewed distribution [J]. China Safety Science Journal, 2019,29(10):160-166.
[16] BRAUERS W K M, ZAVADSKAS E K. Project management by MULTIMOORA as an instrument for transition economies [J].Technological & Economic Development of Economy, 2010,16(1):5-24.
[17] BRAUERS W K M. Project management for a country with multiple objectives [J]. Czech Econmic Review, 2012,6(1):80-101.
[18] BRAUERS W K M, ZAVADSKAS E K. Robustness of MULTIMOORA: a method for multi-objective optimization [J]. Informatica, 2012,23(1):1-25.