基于结构冗余传感器配置与灰狼优化算法的无人机可诊断性优化设计

doi:10.12305/j.issn.1001-506X.2025.07.22

Abstract

Abstract:

To improve the diagnosability of unmanned aerial vehicles (UAVs), this paper proposes an optimal design strategy for diagnosability based on structural redundant sensor configurations and the grey wolf optimization algorithm. Firstly, to make up for the defect of structural analysis in measuring the difficulty of fault diagnosis, a quantitative evaluation method of diagnosability based on Wasserstein distance is proposed. Secondly, a structural redundant sensor configuration algorithm is designed to maximize the system's diagnosability with the lowest sensor configuration cost. Finally, a diagnosability optimization design strategy based on the gray wolf optimization algorithm is proposed to minimize the design cost of the diagnosability system while meeting the qualitative and quantitative diagnosability requirements. Based on the fixed UAV structural model, using the proposed algorithm, the system detectability and isolation rate reaches 100% with the minimum cost of optimal sensor configuration. The optimization strategy based on qualitative evaluation makes the diagnostic cost shrink by 83%, which is a saving of 2% to 15% compared with the other algorithms. The optimization strategy based on quantitative evaluation makes the diagnostic cost shrink by 90%, which is a saving of 0% to 25% compared with the other algorithms.

Key words: structural analysis, diagnosability, sensor configuration, grey wolf optimization algorithm (GWO)

CLC Number:

V240.2

Xuping GU, Xianjun SHI. Diagnosability optimal design of UAV based on structurally redundant sensor configurations with grey wolf optimization algorithm[J]. Systems Engineering and Electronics, 2025, 47(7): 2283-2303.

Figures/Tables 21

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References 26

1	TELLI K , KRAA O , HIMEUR Y , et al. A comprehensive review of recent research trends on unmanned aerial vehicles (UAVs)[J]. Systems, 2023, 11 (8): 400- 412.
2	GARGALAKOS M . The role of unmanned aerial vehicles in military communications: application scenarios, current trends, and beyond[J]. The Journal of Defense Modeling and Simulation, 2021, 10 (4): 154- 168.
3	NAWAZ H , ALI H M , MASSAN S R . Applications of unmanned aerial vehicles: a review[J]. Biomimetics, 2019, 10 (4): 400- 411.
4	DILEEP M R, NAVANEETH A V, ULLAGADDI S, et al. A study and analysis on various types of agricultural drones and its applications[C]//Proc. of the 5th International Conference on Research in Computational Intelligence and Communication Networks, 2020.
5	GU X P , SHI X J . A Review of research on diagnosability of control systems based on structural analysis[J]. Applied Sciences, 2023, 13 (22): 12241- 12253.
6	GU X P , SHI X J . A review of research on diagnosability of control systems[J]. Energy Reports, 2024, 18 (20): 3945- 3951.
7	PEREZ-ZUNIGA G , RIVAS-PEREZ R , SOTOMAYOR-MORIANO J , et al. Fault detection and isolation system based on structural analysis of an industrial seawater reverse osmosis desalination plant[J]. Processes, 2020, 8 (9): 1100- 1118.
8	FU F Z , WANG D Y , LI W B , et al. Overall fault diagnosability evaluation for dynamic systems: a quantitative-qualitative approach[J]. Automatica, 2022, 146 (57): 110591.
9	CHENG Y Q , LIU Y W , YANG S M , et al. Multi-sensor optimal placement of rotor-bearing system based on fault diagnosability[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2023, 237 (6): 1510- 1521.
10	WANG Z L, TANG Z H, CHEN F Y. Quantitative evaluation of sensor fault diagnosability of F-16 high maneuvering fighter[C]// Proc. of the IEEE 5th International Conference on Automation, Electronics and Electrical Engineering, 2022.
11	REPPA V , TIMOTHEOU S , POLYCARPOU M M , et al. Performance index for optimizing sensor fault detection of a class of nonlinear systems[J]. IFAC-PapersOnLine, 2018, 51 (24): 1387- 1394.
12	LIU Q Y , WANG Z D , ZHANG J F , et al. Necessary and sufficient conditions for fault diagnosability of linear open- and closed-loop stochastic systems under sensor and actuator faults[J]. IEEE Trans.on Automatic Control, 2022, 67 (8): 4178- 4185.
13	CUI Y Q , SHI J Y , WANG Z L . System-level operational diagnosability analysis in quasi real-time fault diagnosis: the probabilistic approach[J]. Journal of Process Control, 2014, 24 (9): 1444- 1453.
14	QU R T , JIANG B , CHENG Y H . Research on the diagnosability of a satellite attitude determination system on a fault information manifold[J]. Applied Sciences, 2022, 12 (24): 12835- 12843.
15	YIN C, HE Z M, WANG J Q, et al. A method for fault diagnosability evaluation of spacecraft control system[C]//Proc. of the Joint International Information Technology, Mechanical and Electronic Engineering, 2016.
16	FU F Z , XUE T , WU Z G , et al. A fault diagnosability evaluation method for dynamic systems without distribution knowledge[J]. IEEE Trans.on Cybernetics, 2022, 52 (6): 5113- 5123.
17	STIEFELMAIER J , GIENGER A , BOHM M , et al. A Bayesian approach to fault diagnosability analysis in adaptive structures[J]. IFAC-PapersOnLine, 2022, 55 (27): 347- 352.
18	王晓龙, 付锐棋, 李英晟, 等. 基于动态TDFP模型与Wasserstein距离的滚动轴承运行状态评估[J]. 中国工程机械学报, 2024, 22 (1): 7- 12.
	WANG X L , FU R Q , LI Y S , et al. Running condition assessment of rolling bearings based on dynamic TDFP model and Wasserstein distance[J]. China Journal of Construction Machinery, 2024, 22 (1): 7- 12.
19	康传利, 张思瑶, 李玄皓, 等. 高斯Wasserstein距离改进轻量YOLOv7模型的遥感影像道路交叉口检测[J]. 科学技术与工程, 2024, 24 (9): 3533- 3542.
	KANG C L , ZHANG S Y , LI X H , et al. Gaussian Wasserstein distance improved lightweight YOLOv7 model for road intersection detection in remote sensing images[J]. Science, Technology and Engineering, 2024, 24 (9): 3533- 3542.
20	晏远翔, 曹国, 张友强. 基于Wasserstein距离与生成对抗网络的高光谱图像分类[J]. 计算机系统应用, 2024, 33 (2): 13- 22.
	YAN Y X , CAO G , ZHANG Y Q . Hyperspectral image classification based on Wasserstein distance and generative adversarial network[J]. Computer System Applications, 2024, 33 (2): 13- 22.
21	王志鹏, 杜常清, 胡杰, 等. 基于结构分析的48 V微混系统的故障诊断[J]. 江苏大学学报(自然科学版), 2019, 40 (6): 636- 642.
	WANG Z P , DU C Q , HU J , et al. Fault diagnosis of 48 V microhybrid system based on structural analysis[J]. Journal of Jiangsu University (Natural Science Edition), 2019, 40 (6): 636- 642.
22	丁华, 吴培鸿. 基于结构分析的线控踏板位置传感器故障诊断[J]. 电子设计工程, 2021, 29 (5): 65- 70.
	DING H , WU P H . Fault diagnosis of in-line pedal position sensor based on structural analysis[J]. Electronic Design Engineering, 2021, 29 (5): 65- 70.
23	杨新桦, 谭水平. 基于结构分析法的EMCVT故障诊断与容错控制[J]. 重庆理工大学学报(自然科学), 2022, 36 (8): 134- 145.
	YANG X H , TAN S P . Fault diagnosis and fault-tolerant control of EMCVT based on structural analysis method[J]. Journal of Chongqing University of Technology (Natural Science), 2022, 36 (8): 134- 145.
24	陈奇, 汪金成, AHMEDQ, 等. 基于模型的汽车电动助力转向系统故障诊断[J]. 汽车工程, 2019, 41 (7): 839- 850.
	CHEN Q , WANG J C , AHMED Q , et al. Model-based fault diagnosis of automotive electric power steering system[J]. Automotive Engineering, 2019, 41 (7): 839- 850.
25	CHEN Q , TIAN W F , CHEN W W , et al. Model-based fault diagnosis of an anti-lock braking system via structural analysis[J]. Sensors, 2018, 18 (12): 4468- 4475.
26	KRYSANDER M , ASLUND J , NYBERG M . An efficient algorithm for finding minimal overconstrained subsystems for model-based diagnosis[J]. IEEE Trans.on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2008, 38 (1): 197- 206.

函数表达式	极值	维度	自变量范围
$F_1(\boldsymbol{b})=\sum\limits_{i=1}^n x_i^2$	0	30	[-100, 100]
$F_2(\boldsymbol{b})=\max \|x\|$	0	30	[-100, 100]
$F_3(\boldsymbol{b})=\sum\limits_{i=1}^n i \cdot x_i^4+\mathrm{rand}$	0	30	[-5.12, 5.12]
$\begin{gathered}F_4(\boldsymbol{b})=-20 \mathrm{e}^{-0.2 \sqrt{\frac{\sum\limits_{i=1}^n x_i^2}{n}}}- \\\mathrm{e}^{\frac{\sum\limits_{i=1}^n \cos \left(2 \pi x_i\right)}{n}}+20+\mathrm{e}\end{gathered}$	0	30	[-32, 32]

[1]	Yufeng QIN, Xianjun SHI. Fault diagnosability evaluation method based on multi-signal flow graph and similarity measure [J]. Systems Engineering and Electronics, 2023, 45(1): 302-312.
[2]	YANG Xuan, GUO Fabin, WANG Mingzhe. Analysis of LEO constellation spatial coverage attribute based on Petri nets [J]. Systems Engineering and Electronics, 2014, 36(3): 481-486.
[3]	NING Ning, ZHANG Jun, GAO Xiang-yang, XUE Jing. Formal verification of SDG diagnosability via symbolic model checking [J]. Journal of Systems Engineering and Electronics, 2011, 33(2): 390-394.

Diagnosability optimal design of UAV based on structurally redundant sensor configurations with grey wolf optimization algorithm

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