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http://dx.doi.org/10.5139/JKSAS.2009.37.6.556

A Study on Multi-Fault Diagnosis for Turboshaft Engine of UAV Using Fuzzy and Neural Networks  

Kong, Chang-Duk (조선대학교 항공우주공학과)
Ki, Ja-Young ((주)이지가스터빈 R&D)
Kho, Seong-Hee (조선대학교 항공우주공학과 대학원)
Koo, Young-Ju (조선대학교 항공우주공학과 대학원)
Lee, Chang-Ho (한국항공우주연구원)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.37, no.6, 2009 , pp. 556-561 More about this Journal
Abstract
The UAV(Unmanned Aerial Vehicle) that is remotely operating in various and long flight environments must have a very reliable propulsion system. Precise fault diagnosis of the turbo shaft engine for the Smart UAV that has the vertical take-off, landing and forward flight behaviors can promote reliability and availability. This work proposes a new diagnostic method that can identify the faulted components from engine measuring parameter changes using Fuzzy Logic and quantify its faults from the identified fault pattern using Neural Network Algorithms. The proposed diagnostic method can detect not only single fault but also multiple faults.
Keywords
Feed Forward Back Propagation; Gas Path Analysis; Fuzzy Logic; Neural Network Algorithm;
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