数字孪生驱动的机群寿命精细化管理

doi:10.7527/S1000-6893.2025.31290

Abstract

Abstract:

With the prominence of aircraft flight safety guarantee and the development of single aircraft life monitoring technology， the intelligent and meticulous management of aircraft fleet life has become a key concern. This paper addresses the maintenance planning and flight training planning， the two key elements in the life management of fleet. Firstly， based on the physical information of real-time health status of aircraft structures， a maintenance planning model driven by digital twins is established with the planning objective of maximizing the utilization rate of maintenance resources and fleet retention rate and the constraints of the remaining life and simultaneous maintenance ability of each aircraft structure. Secondly， based on the life loss information， a digital twin-driven flight training planning model is established with the life loss balance and remaining service life of key parts as planning objectives， and the maintenance time and flight training tasks of each aircraft as constraints. By integrating the above models， a set of high-fidelity and refined management method of cluster life is formed， and the practical engineering application of this management method is demonstrated by constructing cluster data.

Key words: digital twins, fleet management, health monitoring, non-dominated sorting genetic algorithms, fleet life

CLC Number:

V215.2⁺7

Yuxuan GU, Cong GUO, Lei HUANG, Yifei DONG, Hongda DONG, Zhilun DENG. Refined management of fleet life driven by digital twins[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531290.

Figures/Tables 11

Fig.1

Fig.2

Table 1

Table 2

Table 3

Fig.3

Fig.4

Table 4

Fig.5

Fig.6

Table 5

References 19

[1]	罗斌. 基于结构疲劳寿命预测的机队维修决策方法研究［D］. 哈尔滨：哈尔滨工业大学， 2018： 1-3.
	LUO B. Research on fleet maintenance decision method based on structural fatigue life prediction［D］. Harbin： Harbin Institute of Technology， 2018： 1-3 （in Chinese）.
[2]	STASZEWSKI W J. Fatigue crack detection using smart sensor technologies［J］. Fatigue & Fracture of Engineering Materials & Structures， 2008， 31（8）： 609-610.
[3]	CHO H， LISSENDEN C J. Structural health monitoring of fatigue crack growth in plate structures with ultrasonic guided waves［J］. Structural Health Monitoring， 2012， 11（4）： 393-404.
[4]	KURNYTA A， LESKI A， DRAGAN K， et al. Health monitoring of the aircraft structure during a full scale fatigue test with use of resistive ladder sensors［J］. Solid State Phenomena， 2015， 220-221： 349-354.
[5]	葛恩顺，李庆民，张光宇，等. 考虑不完全维修的劣化系统最优视情维修策略［J］. 航空学报， 2013， 34（2）： 316-324.
	GE E S， LI Q M， ZHANG G Y， et al. Optimization of condition-based maintenance for degradation systems under imperfect maintenance［J］. Acta Aeronautica et Astronautica Sinica， 2013， 34（2）： 316-324 （in Chinese）.
[6]	ARAMON B M， BECK J C. Scheduling a dynamic aircraft repair shop with limited repair resources［J］. Journal of Artificial Intelligence Research， 2013， 47： 35-70.
[7]	GAVRANIS A， KOZANIDIS G. An exact solution algorithm for maximizing the fleet availability of a unit of aircraft subject to flight and maintenance requirements［J］. European Journal of Operational Research， 2015， 242（2）： 631-643.
[8]	国真真. 基于MILP模型的机库维修调度问题研究［D］. 大连：东北财经大学， 2022： 3-5.
	GUO Z Z. Research on hangar maintenance scheduling based on MILP model［D］. Dalian： Dongbei University of Finance and Economics， 2022： 3-5 （in Chinese）.
[9]	FENG Q， BI X， ZHAO X J， et al. Heuristic hybrid game approach for fleet condition-based maintenance planning［J］. Reliability Engineering & System Safety， 2017， 157： 166-176.
[10]	CHO P， FARIAS V， KESSLER J， et al. Maintenance and flight scheduling of low observable aircraft［J］. Naval Research Logistics （NRL）， 2015， 62（1）： 60-80.
[11]	陶飞，刘蔚然，张萌，等. 数字孪生五维模型及十大领域应用［J］.计算机集成制造系统，2019，25（01）：1-18.
	TAO F， LIU W R， ZHANG M， et al. Five-dimension digital twin model and its ten application［J］. Computer Integrated Manufacturing Systems，2019，25（01）：1-18 （in Chinese）.
[12]	孟松鹤，叶雨玫，杨强，等. 数字孪生及其在航空航天中的应用［J］. 航空学报， 2020， 41（9）： 023615.
	MENG S H， YE Y M， YANG Q， et al. Digital twin and its aerospace applications［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（9）： 023615 （in Chinese）.
[13]	孙侠生，肖迎春. 飞机结构健康监测技术的机遇与挑战［J］. 航空学报， 2014， 35（12）： 3199-3212.
	SUN X S， XIAO Y C. Opportunities and challenges of aircraft structural health monitoring［J］. Acta Aeronautica et Astronautica Sinica， 2014， 35（12）： 3199-3212 （in Chinese）.
[14]	RAMEZANI S， MOINI A， RIAHI M. Prognostics and health management in machinery： A review of methodologies for R prediction and roadmap［J］. International Journal of Industrial Engineering and Management Science， 2019， 6（1）： 38-61.
[15]	QI Q L， TAO F， HU T L， et al. Enabling technologies and tools for digital twin［J］. Journal of Manufacturing Systems， 2021， 58： 3-21.
[16]	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）： 023981.
	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）： 023981 （in Chinese）.
[17]	韩周鹏，刘永，巴黎，等. 数字孪生驱动的连铸辊健康监测与组装优化框架［J］. 制造业自动化， 2023， 45（4）： 1-5.
	HAN Z P， LIU Y， BA L， et al. Framework for health monitoring and assembly optimization of casting roller in continuous casting machine based on digital twin［J］. Manufacturing Automation， 2023， 45（4）： 1-5 （in Chinese）.
[18]	郭丞皓，于劲松，宋悦，等. 基于数字孪生的飞机起落架健康管理技术［J］. 航空学报， 2023， 44（11）： 227629.
	GUO C H， YU J S， SONG Y， et al. Application of digital twin-based aircraft landing gear health management technology［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（11）： 227629 （in Chinese）.
[19]	田阔，孙志勇，李增聪. 面向结构静力试验监测的高精度数字孪生方法［J］. 航空学报， 2024， 45（7）： 429134.
	TIAN K， SUN Z Y， LI Z C. High-precision digital twin method for structural static test monitoring［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（7）： 429134 （in Chinese）.

飞机编号	出厂时间	飞机编号	出厂时间
1	20141205	11	20200331
2	20141225	12	20200331
3	20200331	13	20200331
4	20200331	14	20200331
5	20200331	15	20200331
6	20200331	16	20200331
7	20200331	17	20200331
8	20200331	18	20200331
9	20200331	19	20201112
10	20200331	20	20201112

飞机编号	剩余寿命/h
飞机编号	部位1	部位2	部位3	部位4	部位5	部位6	部位7	部位8	部位9	部位10	整机
1	55	51	72	56	69	67	74	51	68	54	51
2	106	110	111	108	112	113	116	120	121	118	106
3	159	152	145	150	178	176	177	168	163	160	145
4	212	204	210	205	230	228	218	195	212	218	195
5	231	238	226	232	239	227	241	248	236	242	226
6	341	344	339	344	348	343	351	354	349	354	339
7	564	553	579	572	556	549	553	552	568	574	549
8	583	608	579	585	610	581	593	618	589	595	579
9	615	64	645	616	641	646	625	650	655	626	615
10	636	654	655	647	619	645	652	639	623	628	619
11	888	931	884	890	933	886	898	941	894	900	884
12	944	907	906	949	912	911	954	917	916	959	906
13	930	925	918	932	929	926	945	929	917	935	917
14	1 110	1 063	1 146	1 112	1 065	1 114	1 120	1 073	1 156	1 122	1 063
15	1 117	1 100	1 118	1 131	1 131	1 124	1 102	1 121	1 113	1 103	1 100
16	1 185	1 176	1 173	1 165	1 185	1 194	1 181	1 172	1 195	1 190	1 165
17	1 336	1 359	1 398	1 338	1 361	1 400	1 346	1 369	1 408	1 348	1 336
18	1 397	1 379	1 407	1 398	1 377	1 394	1 407	1 389	1 390	1 408	1 377
19	1 408	1 413	1 470	1 411	1 415	1 173	1 418	1 423	1 480	1 421	1 408
20	1 492	1 470	1 475	1 488	1 496	1 482	1 489	1 481	1 493	1 494	1 470

任务编号	寿命损耗/h
任务编号	部位1	部位2	部位3	部位4	部位5	部位6	部位7	部位8	部位9	部位10
1	1.61	0.79	1.38	1.73	0.97	1.08	0.75	1.20	0.71	1.06
2	1.61	1.68	0.75	1.63	0.86	1.40	1.98	1.51	0.96	0.95
3	0.96	1.35	0.88	1.64	1.78	1.56	0.87	1.40	1.60	0.98
4	1.83	1.82	1.17	1.85	1.63	1.17	1.34	1.78	1.71	1.48
5	1.31	1.55	1.03	1.01	1.58	1.32	1.31	1.70	1.44	0.94
6	1.44	1.07	1.53	0.79	0.91	1.80	1.74	1.95	1.25	1.11
7	1.02	1.65	1.45	1.58	1.96	0.82	1.05	1.05	1.57	1.88
8	0.90	0.76	1.77	0.79	1.04	1.03	0.75	0.74	1.48	1.32
9	1.25	1.09	0.72	0.79	1.04	1.03	0.98	1.80	1.61	0.89
10	1.61	1.85	1.98	1.20	1.83	1.98	1.17	1.73	1.40	1.70

飞机编号	维修日期	飞机编号	维修日期
1	20240725	11	20271109
2	20240623	12	20280207
3	20250330	13	20280706
4	20250302	14	20281008
5	20251030	15	20290321
6	20260205	16	20290616
7	20260706	17	20300216
8	20261012	18	20300213
9	20270304	19	20301109
10	20270612	20	20301013

Refined management of fleet life driven by digital twins

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 19

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Jianzhong SUN, Zhuojian WANG, Hongsheng YAN, Zhe LI, Sizheng DUAN, Hanchun HU, Hongfu ZUO. Research advances in aircraft predictive maintenance [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(7): 30852-030852.
[2]	Shenfang YUAN, Qiuhui XU, Jian CHEN. Reliability evaluation: From non-destructive testing to structural health monitoring [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(5): 531442-531442.
[3]	Liang CHEN, Lei HUANG, Yuxuan GU, Cong GUO, Kexin LIN, Yu GUAN, Jian SONG. Twinning technology of key part load based on flight parameters [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 531292-531292.
[4]	Pengfei WANG, Lifang ZENG, Xueming SHAO, Jun LI. Multi-source data fusion modeling method for aerodynamic load of aircraft wing based on pre-training and fine-tuning [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 532297-532297.
[5]	Ran ZHUO, Chuliang YAN. A key component in digital twin of aircraft structures: Multi-dimensional flight parameter measurements [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 532375-532375.
[6]	Guoqiang YU, Siyuan HAN, Xiguang GAO, Yingdong SONG. Application and development prospects of electrical impedance imaging in aerospace structural health monitoring [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(15): 231532-231532.
[7]	Shuming YANG, Jianjun WU, Changlin XIE, Yuqiang CHENG, Biao WANG. Application issues of data-driven intelligent fault diagnosis technologies for liquid rocket engines [J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(15): 131427-131427.
[8]	Yingzhi ZHANG, Huibin SUN, Cheng YAN, Meng LIU. A digital twin model for rotor tip clearance prediction considering interval uncertainty [J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(21): 629775-629775.
[9]	SHI Huaitao, LIU Zimeng, BAI Xiaotian, MA Hui. Crack location recognition method for full ceramic bearing outer ring [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 625481-625481.
[10]	ZHENG Hui, QIU Lei, YUAN Shenfang, YANG Xiaofei, LU Xulong, XUE Zhaopeng. Experimental method of guided wave monitoring for high temperature airflow damage of C/C thermal protection structures [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 225659-225659.
[11]	WANG Yupeng, LYU Shuaishuai, YANG Yu, LI Jiaxin, WANG Yezi. Damage recognition of composite structures based on domain adaptive model [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(6): 526752-526752.
[12]	YUE Caixu, ZHANG Juntao, LIU Xianli, CHEN Zhitao, Steven Y. LIANG, Lihui WANG. Research progress on machining deformation of thin-walled parts in milling process [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(4): 525164-525164.
[13]	QU Shuxuan, GONG Wenbin, SUN Xiaozhu, ZHANG Dongxing, LIANG Zhiqiang, GAO Limin, LYU Weibang. On-line damage monitoring of composites based on carbon nanotube films [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(1): 424949-424949.
[14]	JIAO Jingpin, LI Haiping, ZHAI Shuncheng, HE Cunfu, WU Bin. Lamb waves health monitoring technology for metal stiffened plate based on compressive sensing [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(7): 422695-422695.
[15]	CHEN Jian, YUAN Shenfang, WANG Hui, QIU Lei. Using Gaussian weighting-mixture proposal distribution particle filter for fatigue crack growth prediction [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017, 38(11): 220925-220925.

任务编号	飞机编号
任务编号	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20
1											√
2												√
3
4															√		√
5
6											√
7											√	√
8
9
10												√					√