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Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

[+] Author Affiliations
Changduk Kong, Semyeong Lim, Keonwoo Kim

Chosun University, Kwangju, Republic of Korea

Paper No. GT2012-68158, pp. 19-30; 12 pages
doi:10.1115/GT2012-68158
From:
  • ASME Turbo Expo 2012: Turbine Technical Conference and Exposition
  • Volume 3: Cycle Innovations; Education; Electric Power; Fans and Blowers; Industrial and Cogeneration
  • Copenhagen, Denmark, June 11–15, 2012
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4469-4
  • Copyright © 2012 by ASME

abstract

Recently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks, Fuzzy Logic and Genetic Algorithms have been studied to improve the model based engine diagnostic methods. Among them the Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if only use of the Neural Networks. In addition, it has a very complex structure due to finding effectively faults of single type faults and multiple type faults of gas path components.

This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data.

The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP(Feed Forward Back Propagation) is used. In order to user’s friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB.

The proposed program is verified by application of several case studies having the arbitrary implanted engine component faults as well as real engine performance data.

Copyright © 2012 by ASME

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