机理与生成对抗代理建模融合的飞机系统可靠性评估

doi:10.7527/S1000-6893.2024.30948

Abstract

Abstract:

To effectively perform aircraft system reliability evaluation， the Mechanism and Data dual-drive Reliability Monitoring （MDRM） concept has been proposed. In the MDRM concept， the fault logic diagram is constructed from the perspective of forward mechanism based on the Functional Hazard Analysis （FHA）， Failure Mode and Effects Analysis （FMEA）， and Fault Tree Analysis （FTA）. By integrating optional data， a Bayesian network model is built to select important influencing parameters. The generative adversarial theory is introduced into the surrogate model， and a generative adversarial surrogate modeling strategy is presented to establish a correlation model between the influencing parameters and research object， thus enabling the reliability evaluation. The Generative Adversarial Regression Network （GARN） method is proposed for aircraft systems reliability evaluation by integrating the MDRM concept with neural network models and compact support region thought. In addition， the mathematical cases are adopted to demonstrate the modeling performance of proposed GARN， and the engineering applicability of developed method are verified through the No.1 hydraulic system low-pressure and landing gear brake temperature multi-failures of a domestic civil aircraft. The comparison of several methods shows that GARN holds outstanding modeling and simulation performance advantages， and the proposed concept and method can provide strong theoretical and technical support for aircraft system reliability assessment.

Key words: mechanism and data dual-drive, aircraft system, reliability evaluation, generative adversarial surrogate modeling, hydraulic system low-pressure, landing gear brake temperature multi-failures

CLC Number:

V266

Yunwen FENG, Da TENG, Cheng LU, Rui WANG, Junyu CHEN. Integrated mechanism and generative adversarial surrogate modeling for aircraft systems reliability evaluation[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(7): 230948.

Figures/Tables 31

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 1

Fig.9

Fig.10

Fig.11

Fig.12

Table 2

Fig.13

Fig.14

Fig.15

Table 3

Fig.16

Fig.17

Fig.18

Fig.19

Fig.20

Table 4

Fig.21

Fig.22

Fig.23

Fig.24

Fig.25

Fig.26

Fig.27

References 28

1	周长聪，吉梦瑶，张屹尚，等. 多失效模式下起落架机构可靠性及灵敏度研究［J］. 西北工业大学学报， 2021， 39（1）： 46-54.
	ZHOU C C， JI M Y， ZHANG Y S， et al. Mechanism reliability and sensitivity analysis of landing gear under multiple failure modes［J］. Journal of Northwestern Polytechnical University， 2021， 39（1）： 46-54 （in Chinese）.
2	PANG H， YU T X， SONG B F. Failure mechanism analysis and reliability assessment of an aircraft slat［J］. Engineering Failure Analysis， 2016， 60： 261-279.
3	陈艺夫，马宇航，蓝庆生，等. 基于多项式混沌法的翼型不确定性分析及梯度优化设计［J］. 航空学报， 2023， 44（8）： 67-88.
	CHEN Y F， MA Y H， LAN Q S， et al. Uncertainty analysis and gradient optimization design of airfoil based on polynomial chaos expansion method［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（8）： 67-88 （in Chinese）.
4	ZHANG W， WANG Q， ZENG F Z， et al. A novel robust aerodynamic optimization technique coupled with adjoint solvers and polynomial chaos expansion［J］. Chinese Journal of Aeronautics， 2022， 35（10）： 35-55.
5	LUO C Q， ZHU S P， KESHTEGAR B， et al. Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy［J］. Computer Methods in Applied Mechanics and Engineering， 2024， 423： 116863.
6	ZHANG H， SONG L K， BAI G C， et al. Active extremum Kriging-based multi-level linkage reliability analysis and its application in aeroengine mechanism systems［J］. Aerospace Science and Technology， 2022， 131： 107968.
7	LUO C Q， ZHU S P， KESHTEGAR B， et al. An enhanced uniform simulation approach coupled with SVR for efficient structural reliability analysis［J］. Reliability Engineering & System Safety， 2023， 237： 109377.
8	杨倩，郭晓峰，李芹，等. 基于POD和代理模型的热气防冰性能预测方法［J］. 航空学报， 2023， 44（1）： 626992.
	YANG Q， GUO X F， LI Q， et al. Hot air anti-icing performance estimation method based on POD and surrogate model［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（1）： 626992 （in Chinese）.
9	ZHU S P， NIU X P， KESHTEGAR B， et al. Machine learning-based probabilistic fatigue assessment ofturbine bladed disks under multisource uncertainties［J］. International Journal of Structural Integrity， 2023， 14（6）： 1000-1024.
10	陈松坤，王德禹. 基于神经网络的蒙特卡罗可靠性分析方法［J］. 上海交通大学学报， 2018， 52（6）： 687-692.
	CHEN S K， WANG D Y. An improved Monte Carlo reliability analysis method based on neural network［J］. Journal of Shanghai Jiao Tong University， 2018， 52（6）： 687-692 （in Chinese）.
11	陈保家，邱光银，肖文荣，等. 航空发动机转子轴承运行可靠性评估方法［J］. 西安交通大学学报， 2018， 52（10）： 41-48.
	CHEN B J， QIU G Y， XIAO W R， et al. An evaluation method of operational reliability for aero-engine rotor bearings［J］. Journal of Xi’an Jiaotong University， 2018， 52（10）： 41-48 （in Chinese）.
12	刘磊，腾达，冯蕴雯. 基于协同智能移动Kriging的襟翼偏角可靠性分析［J］. 西北工业大学学报， 2023， 41（2）： 253-263.
	LIU L， TENG D， FENG Y W. Reliability analysis of flap deflection angle based on collaborative intelligent moving Kriging model［J］. Journal of Northwestern Polytechnical University， 2023， 41（2）： 253-263 （in Chinese）.
13	LEE H， LI G Y， RAI A， et al. Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft［J］. Advanced Engineering Informatics， 2020， 44： 101071.
14	冯蕴雯，潘维煌，刘佳奇，等. 基于机器学习的飞机动力装置运行可靠性［J］. 航空学报， 2021， 42（4）： 524732.
	FENG Y W， PAN W H， LIU J Q， et al. Operational reliability of aircraft power plant based on machine learning［J］. Acta Aeronautica et Astronautica Sinica， 2021， 42（4）： 524732 （in Chinese）.
15	刘佳奇，冯蕴雯，路成，等. 基于智能神经网络的航空发动机运行安全分析［J］. 航空学报， 2022， 43（9）： 625375.
	LIU J Q， FENG Y W， LU C， et al. Safety analysis of aero-engine operation based on intelligent neural network［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（9）： 625375 （in Chinese）.
16	PANDIAN G， PECHT M， ZIO E， et al. Data-driven reliability analysis of Boeing 787 Dreamliner［J］. Chinese Journal of Aeronautics， 2020， 33（7）： 1969-1979.
17	ZHANG H， XU T， LI H S， et al. StackGAN： Realistic image synthesis with stacked generative adversarial networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2019， 41（8）： 1947-1962.
18	张晟斐，李天梅，胡昌华，等. 基于深度卷积生成对抗网络的缺失数据生成方法及其在剩余寿命预测中的应用［J］. 航空学报， 2022， 43（8）： 225708.
	ZHANG S F， LI T M， HU C H， et al. Missing data generation method and its application in remaininguseful life prediction based on deep convolutional generativeadversarial network［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（8）： 225708 （in Chinese）.
19	GUI J， SUN Z N， WEN Y G， et al. A review on generative adversarial networks： Algorithms， theory， and applications［J］. IEEE Transactions on Knowledge and Data Engineering， 2023， 35（4）： 3313-3332.
20	WATHEN A J， ZHU S X. On spectral distribution of kernel matrices related to radial basis functions［J］. Numerical Algorithms， 2015， 70（4）： 709-726.
21	TIAN M， WANG W J. Some sets of orthogonal polynomial kernel functions［J］. Applied Soft Computing， 2017， 61： 742-756.
22	SATRIA PALAR P， RIZKI ZUHAL L， SHIMOYAMA K. Gaussian process surrogate model with composite kernel learning for engineering design［J］. AIAA Journal， 2020， 58（4）： 1864-1880.
23	LI Z W， LIU X F， DAI J H， et al. Measures of uncertainty based on Gaussian kernel for a fully fuzzy information system［J］. Knowledge-Based Systems， 2020， 196： 105791.
24	LU C， FENG Y W， TENG D. EMR-SSM： Synchronous surrogate modeling-based enhanced moving regression method for multi-response prediction and reliability evaluation［J］. Computer Methods in Applied Mechanics and Engineering， 2024， 421： 116812.
25	TENG D， FENG Y W， LU C， et al. Vectorial generative adversarial surrogate modeling reliability evaluation framework for engineering structural systems［J］. Reliability Engineering & System Safety， 2024， 247： 110076.
26	KAROLCZUK A， KUREK M. Fatigue life uncertainty prediction using the Monte Carlo and Latin hypercube sampling techniques under uniaxial and multiaxial cyclic loading［J］. International Journal of Fatigue， 2022， 160： 106867.
27	GAO P X， YU T， ZHANG Y L， et al. Vibration analysis and control technologies of hydraulic pipeline system in aircraft： A review［J］. Chinese Journal of Aeronautics， 2021， 34（4）： 83-114.
28	LI Y， COOLEN F P A， ZHU C C， et al. Reliability assessment of the hydraulic system of wind turbines based on load-sharing using survival signature［J］. Renewable Energy， 2020， 153： 766-776.

输入变量	均值	标准差
x₁	3	0.1
x₂	4	0.1
x₃	2.5	0.1
x₄	5	0.1
x₅	0.5	0.1
x₆	-0.5	0.1

输入变量	缩写	均值	标准差
1号液压系统油量/%	q_t	53.74	0.95
1号液压系统温度/℃	T_h	34.55	3.79
左侧发动机转速/（r·min^-1）	N₂	87.47	10.82
空速/Kts	V	271.11	135.13
发动机燃油流量/Pph	F	2 127.82	1 201.62

输入变量	符号	左侧或右侧刹车温度有关	均值	标准差
左内侧刹车压力/Pa	p_li	左侧	133.69	96.35
左外刹车压力/Pa	p_lo	左侧	134.64	93.69
左内侧轮速/Kts	v_li	左侧	13.18	5.04
左外侧轮速/Kts	v_lo	左侧	13.09	5.09
右内侧刹车压力/Pa	p_ri	右侧	162.28	111.95
右外刹车压力/Pa	p_ro	右侧	141.61	101.38
右内侧轮速/Kts	v_ti	右侧	11.59	5.28
右外侧轮速/Kts	v_ro	右侧	11.55	5.27
横向加速度/g	l_g	左侧和右侧	0.009 4	0.008 5
马赫数	Ma	左侧和右侧	0.002 8	0.024 1

[1]	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.
[2]	LI Yaohua, GONG Ziyu. Safety analysis of civil aircraft system based on improved FRAM [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(12): 324083-324083.
[3]	YAO Jin-yong;JIANG Tong-min. Evaluation and Analysis of Reliability of ARM Based Embedded Systems Tested by Reliability Enhancement Test [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2006, 27(5): 830-834.
[4]	LIU Zhi-yang;HUANG Min;ZHAO Yu. Integrated Evaluation of System Reliability Using Component's Varied Population-Environment Data [J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2004, 25(3): 254-257.

Integrated mechanism and generative adversarial surrogate modeling for aircraft systems reliability evaluation

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 31

References 28

Related Articles 4

Recommended Articles

Metrics

Comments