Journal of the Korean Society for Aeronautical & Space Sciences (한국항공우주학회지)

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
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A Study on Intelligent Performance Diagnostics of a Gas Turbine Engine Using Neural Networks

신경회로망을 이용한 가스터빈 엔진의 지능형 성능진단에 관한 연구

  • 공창덕 (조선대학교 항공 조선 공학부) ;
  • 고성희 (조선대학교 항공우주공학과 대학원) ;
  • 기자영 (조선대학교 항공 조선 공학부)
  • Published : 2004.04.01
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Abstract

An intelligent performance diagnostic computer program of a gas turbine using the NN(Neural Network) was developed. Recently on-condition performance monitoring of major gas path components using the GPA(Gas Path Analysis) method has been performed in analyzing of engine faults. However because the types and severities of engine faults are various and complex, it is not easy that all fault conditions of the engine would be monitored only by the GPA approach Therefore in order to solve this problem, application of using the NNs for learning and diagnosis would be required. Among then, a BPN (Back Propagation Neural Network) with one hidden layer, which can use an updating learning rate, was proposed for diagnostics of PT6A-62 turboprop engine in this work.

본 연구에서는 신경회로망을 이용한 가스터빈 엔진의 지능형 성능 진단 컴퓨터 프로그램을 개발하였다. 최근에는 엔진 손상을 분석하는데 있어서 주요 구성품의 가스 경로를 실시간 모니터링(monitoring)하는 가스경로해석 (GPA, Gas Path Analysis)방법이 사용되고 있다. 그러나 엔진손상의 형태나 정도가 다양하고 복잡하기 때문에 가스경로해석 접근법만 가지고서는 엔진의 손상상태를 모두 모니터링하기란 쉽지 않다. 따라서 이 문제를 해결하기 위해 학습과 진단을 할 수 있는 신경회로망을 적용하였다. 본 연구에서는 PT6A-62 터보프롭 엔진의 진단에 1개의 은닉층을 갖는 역전파 신경회로망(BPN, Back Propagation Neural Network)이 제안되었다.

Keywords

References

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