Yixiong Yu*
Sound & Vibration, Vol.53, No.5, pp. 237-243, 2019, DOI:10.32604/sv.2019.07887
Abstract Mining aeroengine operational data and developing fault diagnosis
models for aeroengines are to avoid running aeroengines under undesired conditions.
Because of the complexity of working environment and faults of aeroengines,
it is unavoidable that the monitored parameters vary widely and possess
larger noise levels. This paper reports the extrapolation of a diagnosis model
for 20 gas path faults of a double-spool turbofan civil aeroengine. By applying
support vector machine (SVM) algorithm together with genetic algorithm (GA),
the fault diagnosis model is obtained from the training set that was based on
the deviations of the monitored More >
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