Multi-Source Data Fusion for Health Monitoring of Unmanned Aerial Vehicle Structures

HE Xufei; AI Jianliang; SONG Zhitao

doi:10.21656/1000-0887.380225

Volume 39 Issue 4

Turn off MathJax

Article Contents

Article Navigation > Applied Mathematics and Mechanics > 2018 > 39(4): 395-402

HE Xufei, AI Jianliang, SONG Zhitao. Multi-Source Data Fusion for Health Monitoring of Unmanned Aerial Vehicle Structures[J]. Applied Mathematics and Mechanics, 2018, 39(4): 395-402. doi: 10.21656/1000-0887.380225

Citation:

PDF( 1532 KB)

Multi-Source Data Fusion for Health Monitoring of Unmanned Aerial Vehicle Structures

doi: 10.21656/1000-0887.380225

¹Department of Aeronautics and Astronautics, Fudan University, Shanghai 200443, P.R.China; ²Shanghai Aircraft Airworthiness Certification Center of CAAC, Shanghai 200335, P.R.China

Funds: China Postdoctoral Science Foundation（2015M580956）

Received Date: 2017-08-08
Rev Recd Date: 2017-09-14
Publish Date: 2018-04-15

Abstract

Abstract

Structural health monitoring is an important means to guarantee the continuing safe operation of aircrafts, and makes a key technique for unmanned aerial vehicles’ (UAVs’) development and certification. For a UAV fuselage, the structural acceleration responses, strain signals and modal parameters were acquired on-line from different sensor measurements in dynamic structure simulation. The normalized wavelet packet energy change rate index, the strain energy change rate index, the modal frequency change rate index and the mixed damage evaluation indices were built to indicate the structural health condition. The integrated multi-source data fusion technique, including data-level fusion, feature-level fusion and Bayesian probabilistic neural network-based decision-level fusion, was used with the rough set reduction successively to significantly decrease the spatial dimension of data. The mapping between structural damage information, like damage severity, damage locations and health evaluation indices, was established, and the comprehensive decision of the structural damage model was achieved. An example for the health monitoring of an unmanned helicopter was demonstrated. The experimental results verify the accuracy of the proposed data fusion technique for damage identification of multi-damage aircraft structures, and show the validity of multi-data fusion in UAV health monitoring.
- health monitoring,
- data fusion,
- probabilistic neural network,
- rough set reduction,
- damage identification

FullText(HTML)

References(15)

References

[1]	LIANG Dong, YUAN Shenfang. Structural health monitoring system based on multi-agent coordination and fusion for large structure[J]. Advances in Engineering Software,2015,86: 1-12.
[2]	LI Hui, OU Jinping. Structural health monitoring: from sensing technology stepping to health diagnosis[J]. Procedia Engineering,2011,14: 753-760.
[3]	颜王吉, 曹诗泽, 任伟新. 结构系统识别不确定性分析的Bayes方法及其进展[J]. 应用数学和力学, 2017,38(1): 44-59.（YAN Wangji, CAO Shize, REN Weixin. Uncertainty quantification for system identification utilizing the Bayesian theory and its recent advances[J]. Applied Mathematics and Mechanics,2017,38(1): 44-59.(in Chinese)）
[4]	杨君坦, 邱志平, 李琦. 含区间参数不确定结构的损伤识别方法[J]. 北京航空航天大学学报, 2016,42(7): 1509-1517.（YANG Juntan, QIU Zhiping, LI Qi. Damage detection of structures with interval uncertain parameters[J]. Journal of Beijing University of Aeronautics and Astronautics,2016,42(7): 1509-1517.(in Chinese)）
[5]	GUO H Y. Structural damage detection using information fusion technique[J]. Mechanical Systems and Signal Processing,2006,20(5): 1173-1188.
[6]	季赛, 潘锦基, 孙亚杰. 面向结构损伤信号的多传感器感知融合方法[J].数据采集与处理, 2015,30(4): 857-869.（JI Sai, PAN Jinji, SUN Yajie. Multi-sensor data fusion based on compressed sensing for wireless structural damage signal[J]. Journal of Data Acquisition & Processing,2015,30(4): 857-869.(in Chinese)）
[7]	ZONTA D. Sensor data analysis, reduction and fusion for assessing and monitoring civil infrastructures[M]//Sensor Technologies for Civil Infrastructures . Woodhead Publishing, 2014: 33-66.
[8]	TIBADUIZA D A, TORRES-ARREDONDO M A, MUJICA L E,et al. A study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring[J]. Mechanical Systems and Signal Processing,2013,41(1/2): 467-484.
[9]	FENG Yahui, SCHLINDWEIN F S. Normalized wavelet packets quantifiers for condition monitoring[J]. Mechanical Systems and Signal Processing,2009,23(3): 712-723,
[10]	肖书敏，闫云聚，姜波澜. 基于小波神经网络方法的桥梁结构损伤识别研究[J]. 应用数学和力学, 2016,37(2): 149-159.（XIAO Shumin, YAN Yunju, JIANG Bolan. Damage identification for bridge structures based on the wavelet neural network method[J]. Applied Mathematics and Mechanics,2016,37(2): 149-159.(in Chinese)）
[11]	RABIEI M, MODARRES M. A recursive Bayesian framework for structural health management using online monitoring and periodic inspections[J]. Reliability Engineering & System Safety,2013,112: 154-164.
[12]	JIANG S F, FU C, ZHANG C. A hybrid data-fusion system using modal data and probabilistic neural network for damage detection[J]. Advances in Engineering Software,2011,42(6): 368-374.
[13]	JARAMILLO V H, OTTEWILL J R, DUDEK R, et al. Condition monitoring of distributed systems using two-stage Bayesian inference data fusion[J]. Mechanical Systems and Signal Processing,2017,87: 91-110.
[14]	TZORTZIS G, LIKAS A. The minmax k-means clustering algorithm[J]. Pattern Recognition,2014,47(7): 2505-2516.
[15]	EUIWOONG L, MELANIE S, JOHN W. Improved and simplified in approximability for k-means[J]. Information Processing Letters,2017,120: 40-43.