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The Scientific World Journal
Volume 2014, Article ID 727359, 7 pages
http://dx.doi.org/10.1155/2014/727359
Research Article

Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure

1Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2Aviation Technical Division, Aviation Safety Technology Center, 557 Yongyu-ro, Jung-gu, Incheon 400-420, Republic of Korea
3Department of Aerospace, Automobile & Mechanical Engineering, Chung Cheong University, 38 Wolgok-Gil, Gangnae-Myeon, Cheongwon-Gun, Chungcheongbuk-Do 363-792, Republic of Korea

Received 4 April 2014; Accepted 10 June 2014; Published 26 June 2014

Academic Editor: T. O. Ting

Copyright © 2014 Sanghyuk Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure. Twelve available coefficients among healthy and faulty status data group were used to determine the decision. Similarity measure weighting was done and obtained through random forest algorithm (RFA); RF provides data priority. In order to get a fast response of decision, a limited number of coefficients was also considered. Relation of detection rate and amount of feature data were analyzed and illustrated. By repeated trial of similarity calculation, useful data amount was obtained.