基于运行数据的国产民机动态维修任务间隔优化

doi:10.12305/j.issn.1001-506X.2023.04.33

Abstract

Abstract:

To improve the planned maintenance task interval optimization technology of domestic civil aircraft based on operational data and enrich the maintenance interval optimization theory of domestic civil aircraft maintenance outline, a dynamic maintenance interval optimization method of optimal failure distribution based on operational data for the component life characteristics of different maintenance tasks is proposed. The routine inspection and unscheduled inspection maintenance records of the corresponding maintenance tasks are collected. Then the complete failure data and right deletion data of the task components are combined, and the failure distribution parameters within the confidence interval are fitted using the great likelihood estimation to obtain the component life distribution function of the maintenance tasks. The dynamic maintenance intervals of the maintenance tasks are analyzed by considering the accumulated flight hours of the components in conjunction with the reliability requirements of the Civil Aviation Authority advisory circular for different maintenance tasks. The study shows that under the condition of given maintenance task reliability, the maintenance interval of dynamic maintenance tasks can vary with the reliability life law of components, which reflects the life characteristics of corresponding components and helps to provide suggestions for shortening or extending the maintenance task interval on the basis of guaranteeing the task reliability, and provides theoretical reference for the maintenance interval optimization of maintenance tasks.

Key words: domestic civil aircraft, maintenance task interval optimization, task failure distribution, maximum likelihood estimation (MLE), cumulative flight hours

CLC Number:

V267

Yunwen FENG, Weihuang PAN, Cheng LU, Jiaqi LIU. Optimization of dynamic maintenance task interval for domestic civil aircraft based on operation data[J]. Systems Engineering and Electronics, 2023, 45(4): 1231-1238.

Figures/Tables 16

Fig.1

Table 1

Table 2

Table 3

Fig.2

Fig.3

Table 4

Table 5

Table 6

Fig.4

Fig.5

Table 7

Table 8

Table 9

Fig.6

Fig.7

References 31

1	ATA MSG-3. Operator/manufacturer scheduled maintenance volume 1-fixed wing aircraft, vol. 1-fixed[S]. Alexandria: Air Transport Association of America, 2013.
2	柏文华. 民用飞机维修大纲制定的关键技术及方法研究[D]. 南京: 南京航空航天大学, 2014: 12-42.
	BAI W H. Research on major technology and method for civil aircraft MRBR development[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014: 12-42.
3	International Maintenance Review Board Policy Board. IP 180. Aircraft health monitoring (AHM) integration in MSG-3[S]. Shanghai: International Maintenance Review Board Policy Board, 2018.
4	International Maintenance Review Board Policy Board(IMRBPB). IP 44. Evolution/optimization guidelines IMRBPB issue paper 44(Issue 3)[S]. KROHNE: European Aviation Safety Agency, 2011.
5	AHSAN S , LEMMA T , GEBREMARIAM M . Reliability analysis of gas turbine engine by means of bathtub-shaped failure rate distribution[J]. Process Safety Progress, 2020, 39, e12115.
6	SHEHLA R , ROMAN A K . Reliability analysis using an exponential power model with bathtub-shaped failure rate function: a Bayes study[J]. Springer Plus, 2016, 5 (1): 1076. doi: 10.1186/s40064-016-2722-3
7	MUDHOLKAR G S , ASUBONTENG K O , HUTSON A D . Transformation of the bathtub failure rate data in reliability for using Weibull-model analysis[J]. Statistical Methodology, 2009, 6 (6): 622- 633. doi: 10.1016/j.stamet.2009.07.003
8	ALASWAD S , XIANG Y S . A review on condition-based maintenance optimization models for stochastically deteriorating system[J]. Reliability Engineering & System Safety, 2017, 157, 54- 63.
9	JONGE B D . Discretizing continuous-time continuous-state deterioration processes, with an application to condition-based maintenance optimization[J]. Reliability Engineering & System Safety, 2019, 188, 1- 5.
10	YOUSEFI N , COIT D W , SANLING S , et al. Optimization of on-condition thresholds for a system of degrading components with competing dependent failure processes[J]. Reliability Engineering & System Safety, 2019, 192, 106547.
11	YOUSEFI N , TSIANIKAS S , COIT D W . Dynamic maintenance model for a repairable multi-component system using deep reinforcement learning[J]. Quality Engineering, 2022, 34, 16- 35. doi: 10.1080/08982112.2021.1977950
12	YOUSEFI N , TSIANIKAS S , COIT D W . Reinforcement learning for dynamic condition-based maintenance of a system with individually repairable components[J]. Quality Engineering, 2020, 32, 388- 408. doi: 10.1080/08982112.2020.1766692
13	GEORGE B , LOO J , JIE W . Recent advances and future trends on maintenance strategies and optimisation solution techniques for offshore sector[J]. Ocean Engineering, 2022, 250, 110986. doi: 10.1016/j.oceaneng.2022.110986
14	GHORBANI M , NOURELFAIH M , GENDREAUAC M . A two-stage stochastic programming model for selective maintenance optimization[J]. Reliability Engineering & System Safety, 2022, 223, 108480.
15	DENG Q C , BRUNO F S . Lookahead approximate dynamic programming for stochastic aircraft maintenance check scheduling optimization[J]. European Journal of Operational Research, 2022, 299 (3): 814- 833. doi: 10.1016/j.ejor.2021.09.019
16	FRANTZEN M , BANDARU S , NG A . Digital-twin-based decision support of dynamic maintenance task prioritization using simulation-based optimization and genetic programming[J]. Decision Analytics Journal, 2022, 3, 100039. doi: 10.1016/j.dajour.2022.100039
17	STADEN H , DEPREZ L , BOUTE R . A dynamic "predict, then optimize" preventive maintenance approach using operational intervention data[J]. European Journal of Operational Research, 2022, 302 (3): 1079- 1096. doi: 10.1016/j.ejor.2022.01.037
18	WEIDE T V D , DENG Q C , STANTOS B F . Robust long-term aircraft heavy maintenance check scheduling optimization under uncertainty[J]. Computers & Operations Research, 2022, 141, 105667.
19	MA H L , SUN Y G , CHUNG S H , el at . Tackling uncertainties in aircraft maintenance routing: a review of emerging technologies[J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 164, 102805. doi: 10.1016/j.tre.2022.102805
20	AC-91-26. 航空器计划维修要求的编制[S]. 北京: 中国民用航空局, 2015.
	AC-91-26. Preparation of aircraft planned maintenance requirements[S]. Beijing: Civil Aviation Administration of China, 2015.
21	应舒琪. 基于可靠性数据的民机维修间隔优化方法研究[D]. 南京: 南京航空航天大学, 2020: 11-45.
	YING S Q. Research on optimization method of civil aircraft maintenance interval based on reliability data[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2020: 11-45.
22	林聪, 蒋庆喜, 周扬. 基于服役数据的飞机计划维修任务间隔优化方法[J]. 航空工程进展, 2020, 11 (4): 572- 576.
	LIN C , JIANG Q X , ZHOU Y . Schedule maintenance task interval optimization method based on in-service data[J]. Advances in Aeronautical Science and Engineering, 2020, 11 (4): 572- 576.
23	PENG R , HE X F , ZHONG C , et al. Preventive maintenance for heterogeneous parallel systems with two failure modes[J]. Reliability Engineering & System Safety, 2022, 220, 108310.
24	LIU Q N , MA L , WANG N C , et al. A condition-based maintenance model considering multiple maintenance effects on the dependent failure processes[J]. Reliability Engineering & System Safety, 2022, 220, 108267.
25	贾宝惠, 刘彦波, 卢翔, 等. 低利用率下民机结构维修间隔确定模型[J]. 航空学报, 2018, 39 (1): 221516.
	JIA B H , LIU Y B , LU X , et al. Model for determining maintenance intervals of aircraft structural with low utilization[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39 (1): 221516.
26	SI G J , XIA T B , GEBRAEEL N , et al. A reliability-and-cost-based framework to optimize maintenance planning and diverse-skilled technician routing for geographically distributed systems[J]. Reliability Engineering & System Safety, 2022, 226, 108652.
27	HAN Y , ZHEN X W , HUANG Y . Multi-objective optimization for preventive maintenance of offshore safety critical equipment integrating dynamic risk and maintenance cost[J]. Ocean Engineering, 2022, 245, 110557. doi: 10.1016/j.oceaneng.2022.110557
28	WANG J J , WANG Y F , ZHANG Y R , el at . Life cycle dynamic sustainability maintenance strategy optimization of fly ash RC beam based on Monte Carlo simulation[J]. Journal of Cleaner Production, 2022, 351, 131337. doi: 10.1016/j.jclepro.2022.131337
29	ALMALKI S J , YUAN J S . A new modified Weibull distribution[J]. Reliability Engineering & System Safety, 2013, 111 (3): 164- 170.
30	EBELING C E. An introduction to reliability and maintainability engineering[M]. 3rd ed. Dayton: Waveland PressInc, 2019: 58-79.
31	D′AGOSTINO R B , STEPHENS M A . Goodness-of-fit techniques[M]. New York: Routledge, 1987: 194- 234.

故障影响类别	任务描述	检查门槛值/FH	重复检查间隔/FH
9	详细检查后货舱门滑轨机构及锁定机构	3 200	3 200

飞机序号	状态 S/F	来源	累积使用小时/FH	数据类型
1	F	例行定检	2 412	完整数据
1	F	例行定检	2 599	完整数据
2	F	例行定检	5 657	完整数据
2	F	例行定检	6 127	完整数据
2	F	例行定检	8 912	完整数据
2	F	例行定检	9 263	完整数据
⋮	⋮	⋮	⋮	⋮

参数	分布类型
参数	威布尔	Loglogistic	指数
AD	19.070 9	19.076 5	19.357 8

故障影响类别	任务描述	检查门槛值/FH	重复检查间隔/FH
8	操作检查应急出口滑道灯	500	500

飞机序号	状态S/F	来源	累积使用小时/FH	数据类型
1	S	例行定检	1 844.47	右删失
1	S	例行定检	1 844.47	右删失
2	F	非计划检查	1 954.62	完整数据
2	F	非计划检查	358.13	完整数据
2	S	例行定检	3 604.1	右删失
2	S	例行定检	1 291.35	右删失
⋮	⋮	⋮	⋮	⋮

Optimization of dynamic maintenance task interval for domestic civil aircraft based on operation data

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 31

Related Articles 6

Recommended Articles

Metrics

Comments

[1]	Yi LIU, Xiaoxiong ZHOU, Guangjun CHENG. High dynamic carrier tracking technology in frequency hopping systems [J]. Systems Engineering and Electronics, 2022, 44(2): 677-683.
[2]	Yan WANG, Yimin SHI. Statistical analysis of the dependent competing risks model under the double constant-stress accelerated life test [J]. Systems Engineering and Electronics, 2020, 42(11): 2644-2653.
[3]	LIU Yanchao, SHI Yimin, SHI Xiaolin. Reliability analysis of four-unit hybrid systems with masked data [J]. Systems Engineering and Electronics, 2017, 39(5): 1183-1188.
[4]	JIN Yan, GAO Duo, JI Hongbing. Parameter estimation of LFM signal based on robust S transform in α stable distribution noise [J]. Systems Engineering and Electronics, 2017, 39(4): 693-699.
[5]	DI Ruo-hai， GAO Xiao-guang， GUO Zhi-gao. Discrete Bayesian network parameter learning based on monotonic constraint [J]. Systems Engineering and Electronics, 2014, 36(2): 272-277.
[6]	ZHANG Meng， LU Shan， YANG Yang. Reliability estimation of components in system under multiple type-II censoring samples using masked data [J]. Journal of Systems Engineering and Electronics, 2013, 35(5): 1122-1127.