航空公司机队设备可靠性非线性动态评估模型

doi:10.7527/S1000-6893.2013.0013

Abstract

Abstract:

The reliability of the fleet equipment is at the core of an airline company’s effort to achieve its goal of safety, punctuality and economy. The assessment of the reliability of the fleet equipment is an important means to realize the comprehensive technological guarantee of a fleet equipment system. In accordance with the key technology in statistics and data acquisition and the monitoring of the reliability of the fleet equipment, five indicator systems of the fleet equipment reliability, including service difficulty reports (SDR) and the rate of nonscheduled downtime etc., are set up by the Wuli-Shili-Renli (WSR) theory and the Delphi method. In consideration of the features of randomness and volatility of fleet equipment reliability, a nonlinear dynamic assessment model of fleet equipment reliability is designed based on the advantages and disadvantages of grey clustering and back propagation (BP) neural network. The analysis of an application case of the model shows that the nonlinear dynamic assessment model is feasible and applicable to both static and dynamic assessments.

Key words: airline fleet, assessment model, reliability, BP neural network, grey clustering

CLC Number:

V240.2

CHEN Yonggang, LUO Xiaoli. Nonlinear Dynamic Assessment Model of Airline Fleet Equipment Reliability[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(1): 104-111.

References

[1] Deng J G. The airlines fleet plan model. Civil Aviation Economics & Technology, 2000(7): 51-53. (in Chinese) 邓举功. 航空公司机队规划数学模型建立和分析. 民航经济与技术, 2000(7): 51-53.
[2] Peng Y B, Zhang Y L, Zhang X Q. Fleet planning modeling and it’s applications. System Engineering-Theory & Practice, 2001(6): 100-103. (in Chinese) 彭语冰, 张永莉, 张晓全. 机队规划模型的建立及其应用. 系统工程理论与实践, 2001(6): 100-103.
[3] Luo F. Study on formation mechanism of aviation calamity and the early warning system. Wuhan: Wuhan University of Technology, 2004. (in Chinese) 罗帆. 航空灾害成因机理与预警系统研究. 武汉: 武汉理工大学, 2004.
[4] Chen P F, Zuo H F, Wang Y. Application of fuzzy-Bayes method in reliability evaluation of aviation equipment. Aircraft Design, 2008, 28(5): 56-58. (in Chinese) 陈鹏飞, 左洪福, 王晔. 模糊贝叶斯方法在航空设备可靠性评估中的应用. 飞机设计, 2008, 28(5): 56-58.
[5] Cui Y Y, Quan C Y, Ding L P, et al. Model for assessing operational reliability of repairable aircraft systems and its application. Acta Aeronautica et Astronautica Sinica, 2000, 21(4): 346-348. (in Chinese) 崔毅勇, 全成雨, 丁利平, 等. 航空机载可修产品外场可靠性评估模型及其应用. 航空学报, 2000, 21(4): 346-348.
[6] Zhang S F, Cai H. Bayesian reliability assessment of electronic products in aviation. Aerospace Control, 1999(4): 67-71. (in Chinese) 张士峰, 蔡洪. 航空电子产品的BAYES可靠性评估. 航天控制, 1999(4): 67-71.
[7] Zhu H Y, Chen Q H, Han Q. Study on fuzzy synthetic evaluation of supportability of a certain new aircraft equipment. Journal of Qingdao University, 2007, 22(2): 69-72. (in Chinese) 祝华远, 陈庆华, 韩强. 某新型航空装备保障性模糊综合评估方法. 青岛大学学报, 2007, 22(2): 69-72.
[8] Chen Y G. Assessment model of airline fleet reliability based on BP neural network. Mathematics in Practice and Theory, 2011, 41(2): 69-72. (in Chinese) 陈勇刚. 基于BP神经网络的航空公司机队可靠性评价模型. 数学的实践与认识, 2011, 41(2): 69-72.
[9] Guan Q, Deng Z H, Wang S T. Improved fuzzy C-means clustering algorithm. Computer Engineering and Applications, 2011, 47(10): 27-29. (in Chinese) 关庆, 邓赵红, 王士同. 改进的模糊C-均值聚类算法. 计算机工程与应用, 2011, 47(10): 27-29.
[10] Li C X, Yu J. Study on the classification of a kind of fuzzy clustering algorithm. Journal of Beijing Jiaotong University, 2005, 29(2): 17-21. (in Chinese) 李翠霞, 于剑. 一种模糊聚类算法归类的研究. 北京交通大学学报, 2005, 29(2): 17-21.
[11] Wang Z X. Civil aircraft maintenance. 2nd ed. Beijing: Civil Aviation Press of China, 2009: 11-53. (in Chinese) 王再兴. 民用航空器外场维修. 第2版. 北京: 中国民航出版社, 2009: 11-53.
[12] Gao F Z. WSR systems approach and its application. Beijing: The Chinese Academy of Social Sciences,1999. (in Chinese) 高飞著. 物理-事理-人理系统方法及其应用. 北京:中国社会科学学院, 1999.
[13] Liu S F, Xie N M. New grey evaluation method based on reformative triangular whitenization weight function.Journal of Systems Engineering, 2011, 26(2): 244-250. (in Chinese) 刘思峰, 谢乃明. 基于改进三角白化权函数的灰评估新方法. 系统工程学报, 2011, 26(2): 244-250.
[14] Li Y J, Zuo H F, Chen G, et al. Wear particles identification based on grey clustering analysis. Acta Aeronautica et Astronautica Sinica, 2003, 24(4): 373-376. (in Chinese) 李艳军, 左洪福, 陈果, 等. 基于灰色聚类的磨粒自动识别. 航空学报, 2003, 24(4): 373-376.
[15] Liu J J, Wang W, Cheng L. Service level evaluation of signalized intersection based on trapezoid whitenization weight function. Journal of Traffic and Transportation Engineering, 2009, 9(2): 121-126. (in Chinese) 刘俊娟, 王炜, 程琳. 基于梯形白化权函数的信号交叉口服务水平评价. 交通运输工程学报, 2009, 9(2): 121-126.
[16] Dong Y Z, Dang Y G. Grey clustering method based on maximizing deviations. Systems Engineering-Theory & Practice, 2009, 29(9): 141-146. (in Chinese) 董一哲, 党耀国. 基于离差最大化的灰色聚类方法.系统工程理论与实践, 2009, 29(9): 141-146.
[17] Yang T J, Zhang X C, Yang X G, et al. Research on the evaluation of city road traffic safety based on BP artificial network. Journal of China University of Mining, 2009, 34(1): 37-40. (in Chinese) 杨天军, 张晓春, 杨晓光, 等. 基于BP神经网络的城市道路交通安全评价研究. 中国矿业大学学报, 2009, 34(1): 37-40.
[18] Wu Q Z. System engineering. Beijing: Beijing Institute of Technology Press, 2008: 92-93. (in Chinese) 吴祈宗. 系统工程. 北京: 北京理工大学出版社, 2008: 92-93.
[19] Hu S R. Artificial neural network and the application. Changsha: National University of Defense Technology Press, 1998. (in Chinese) 胡守仁. 神经网络应用技术. 长沙: 国防科技大学出版社, 1998.
[20] Song R, Deng B. Application of neural network in safety assessment. China Safety Science Journal, 2005, 15(3): 78-81. (in Chinese) 宋瑞, 邓宝. 神经元网络在安全评价中的应用. 中国安全科学学报, 2005, 15(3): 78-81.
[21] Zhang W, Li X Y, Jiang T N, et al. Life-prediction of multi-stress accelerated life testing based on BP algorithm of artificial neural network. Acta Aeronautica et Astronautica Sinica, 2009, 30(9): 1691-1696. (in Chinese) 张慰, 李晓阳, 姜同敏, 等. 基于BP神经网络的多应力加速寿命试验预测方法. 航空学报, 2009, 30(9): 1691-1696.

Nonlinear Dynamic Assessment Model of Airline Fleet Equipment Reliability

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Feng JIANG, Huacong LI, Jiangfeng FU, Linxiong HONG. A RBF and active learning combined method for structural non-probabilistic reliability analysis [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(2): 226667-226667.
[2]	CHEN Zhiyuan, LI Luyi. Efficient methods for reliability sensitivity analysis of inputs with distribution parameter uncertainty [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 325881-325881.
[3]	LI Xiaoyang, TAO Zhao, ZHANG Wei. Reliability modelling for fatigue based on uncertain differential equation [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 225820-225820.
[4]	DONG Zhenyi, LIN Li, LEI Mingkai, MA Zhiyuan. Ultrasonic quantitative characterization of pore distribution uniformity of seal coating based on BPNN [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(5): 425294-425294.
[5]	HE Yong, WANG Hong, LI Jing, QI Yankun. Joint decision making for condition-based maintenance and spare parts ordering of components based on vibration monitoring [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(5): 225304-225304.
[6]	LEI Shiying, SUN Jianzhong, LIU He. Cumulative damage index model and service reliability evaluation of turbine blade [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(3): 225064-225064.
[7]	WANG Tianshuai, HE Xiaofan, WANG Jinyu, LI Yuhai. Estimation method for DFR value of DED-TA15 titanium alloy based on bimodal lognormal distribution [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(3): 225013-225013.
[8]	ZHOU Chen, SHANG Bolin, SONG Bifeng, KE Erqin, WANG Yaozu. Contribution evaluation of aviation armament system-of-systems based on operation loop [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(2): 224958-224958.
[9]	KANG Rui, WANG Zili. Reliability systems engineering: A research review and prospect [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(10): 527505-527505.
[10]	CAI Ruiyan, LIU Yanhong, WEI Debin. k-terminal reliability analysis of satellite network based on QoS guarantee [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(1): 326020-326020.
[11]	FENG Yunwen, PAN Weihuang, LIU Jiaqi, LU Cheng, XUE Xiaofeng, LENG Jiaxing. Operational reliability of aircraft power plant based on machine learning [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524732-524732.
[12]	LI Jingkui, WANG Ying, WANG Bomin. Spoiler system reliability of certain electric aircraft based on Matrix-GO method [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(3): 623945-623945.
[13]	JIA Beixi, LYU Zhenzhou, LEI Jingyu. Parameterized simulation platform of turbine cooling film blade life reliability analysis [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(12): 224747-224747.
[14]	LUO Junting, ZHAO Jingqi, YANG Zheyi, LIU Weipeng, ZHANG Chunxiang. Microstructure prediction of multi-directional forging of TA15 alloy based on secondary development of Deform and BP neural network [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(12): 424693-424693.
[15]	ZHAO Jian, HUANG Yuechen, LI Haiyang, HE Xiangyue. Uncertainty optimization for return trajectory of vertical takeoff and vertical landing launch vehicle [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(11): 524829-524829.