Fault diagnosis of avionic devices based on information fusion technology

被引：0

作者：

Yan, Tao ^{[1
]}

Zhao, Wen-Jun ^{[1
]}

Hu, Xiu-Jie ^{[2
]}

Song, Jia-You ^{[2
]}

机构：

[1] Aeronautic Electronic Engineering Department, The First Aeronautical College of Air Force, Xinyang, 464000, Henan

[2] School of Information Engineering, Zhengzhou University, Zhengzhou

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2015年 / 44卷 / 03期

关键词：

Avionic device; Evidence theory; Fault diagnosis; Fuzzy neural network; Information fusion;

D O I：

10.3969/j.issn.1001-0548.2015.03.013

中图分类号：

学科分类号：

摘要：

A fault diagnosis scheme for airborne avionics is proposed based on local fault detecting with fuzzy neutral network and decision fusion with Dempster-Shafer evidence theory. Firstly, the characteristic malfunction information of equipment is effectively recombined, and fuzzy neural sub-networks are constructed to achieve independent evidences, with which the diagnosis conclusions as decision fusion results are then drawn by using D-S evidence theory. Lastly, the basic probability values are computed according to the local diagnosis outputs and their credibility. Simulation results indicate that the diagnosis credibility can be obviously increased and the accuracy can also be effectively improved when the scheme is applied to the fault diagnosis of an airborne radio. ©, 2015, Univ. of Electronic Science and Technology of China. All right reserved.

引用

页码：392 / 396

页数：4

共 14 条

[1]

Hu L., Mo C.-Q., Sun Z.-X., Et al., Application of fuzzy theory to fault diagnosis in radar countermeasure reconnaissance receiver, Shipboard Electronic Countermeasure, 35, 6, pp. 18-22, (2012)

[2]

Wen Y., Xiao M.-Q., Hu L.-G., Et al., Research of avionics prognostics based on rough neural network, Computer Measurement&Control, 18, 4, pp. 807-809, (2010)

[3]

Zhang J.-K., Zhang W.-G., Liu X.-X., Et al., Design of a fault-tree-based expert system for flight control system diagnosis, Measurement & Control Technology, 29, 10, pp. 88-99, (2010)

[4]

Guo T.-T., Mu X.-D., Zhang L., Et al., Fault diagnosis method of wireless transmitter based on D-S evidence theory, Computer Engineering and Design, 33, 6, pp. 2506-2510, (2012)

[5]

Xia F., Zhang H., Huang C.-H., Et al., Fault diagnosis on power plant with information fusion technology, IECON 2011-37th Annual Conference on IEEE Industrial Electronics Society, pp. 2370-2375, (2011)

[6]

Wu Y.-H., Li X.-L., Zhang D.-Z., Analysis of aero-engine vibration fault based on D-S evidence theory, Computer Applications and Software, 29, 6, pp. 105-108, (2012)

[7]

Niu G., Han T., Yang S., Et al., Multi-agent decision fusion for motor fault diagnosis, Mechanical Systems and Signal Processing, 21, 3, pp. 1285-1299, (2007)

[8]

Li G.-Y., Intelligent Control and MATLAB Realization, (2005)

[9]

He J.-J., Zhao R., Hydroelectric generating sets fault diagnosis based on information fusion technology, Journal of Central South University, 38, 2, pp. 333-338, (2007)

[10]

Han M., Sun Y.-N., Fan Y.-N., An improced fuzzy neural network based on T-S model, Expert Systems with Applications, 34, 4, pp. 2905-2920, (2008)

← 1 2 →