Data-Driven Based Gas Path Fault Diagnosis for Turbo-Shaft Engine

Abstract:

In order to improve diagnostic accuracy and reduce the rate of misdiagnosis to the aircraft engine gas path faulty, the methods based on data-driven and information fusion are developed and analyzed. BP neural network (NN) and RBF neural network based on data-driven single gas path fault diagnosis method is introduced firstly. Design gas path performance estimators and the fault type classification for turbo-shaft engine. Then the gas path fused diagnostic structure based on D-S evidence theory and least squares support vector machine are developed. Comparisons of the turbo-shaft engine gas path fault diagnosis verify the feasibility and effectiveness of the gas path fault diagnosis based on information fusion.

• Periodical: Applied Mechanics and Materials (Volumes 249-250)
• Pages: 400-404
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.249-250.400
• Online since: December 2012
• Authors: Feng Lu, Tie Bin Zhu, Yi Qiu Lv
• Keywords: Evidence Theory, Gas Path Analysis, Least Squares Support Vector Machines (Ls-SVM), Neural Network (NN), Turbo-Shaft Engine

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