Fault Diagnosis of Aero-engine Lubrication System Based on KPCA-ABC-SVM

In order to efficiently diagnose the mechanical wear failure of aero-engine lubricating oil systems, a base KPCA-ABC-SVM fault diagnosis model is established based on the number of metal abrasive particles considering multiple indicators such as viscosity, temperature, moisture and density. Firstly, the fault detection results obtained by the feature extraction of multi-parameters by kernel principal component analysis (KPCA) method are used as a reference, and then the extracted feature values are classified by the support vector machine (SVM); finally, the penalty factor and kernel function parameters of SVM are optimally selected by using the artificial bee colony (ABC) algorithm to obtain the fault diagnosis with the highest accuracy. Experiments show that support vector machine classification modified by artificial bee colony algorithm can effectively improve the fault detection accuracy after feature extraction.