title: Fault detection of aircraft system with random forest algorithm and similarity measure.
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.
authors: Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
authors: Sanghyuk
authors: 0000-0003-0253-6936
authors: S
authors: Lee
authors: Aviation Technical Division, Aviation Safety Technology Center, 557 Yongyu-ro, Jung-gu, Incheon 400-420, Republic of Korea.
authors: Wookje
authors: W
authors: Park
authors: Department of Aerospace, Automobile & Mechanical Engineering, Chung Cheong University, 38 Wolgok-Gil, Gangnae-Myeon, Cheongwon-Gun, Chungcheongbuk-Do 363-792, Republic of Korea.
authors: Sikhang
authors: S
authors: Jung
mesh_terms: D000401:Aircraft; D000465:Algorithms; D008962:Models, Theoretical