Extrapolation for Aeroengine Gas Path Faults with SVM Bases on Genetic Algorithm

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 parameters superimposed with the noise level of 10%. The SVM model (C = 24.7034; γ = 179.835) was extrapolated for the samples whose noise levels were larger than 10%. The accuracies of extrapolation for samples with the noise levels of 20% and 30% are 97% and 94%, respectively. Compared with the models reported on the same faults, the extrapolation results of the GASVM model are accurate.