Affiliation of Author(s):民航学院
Journal:Beijing Hangkong Hangtian Daxue Xuebao
Abstract:Aircraft engine is wearing during its service life and will finally break down. The wear fault can be diagnosed by analyzing the ferrography data of oil monitoring. The use of immune algorithm optimized support vector machine (SVM) in aircraft engine wear fault diagnosis was researched in this paper. First, the process and algorithm of SVM and immune algorithm were summarized. Then, the optimization of SVM's penalty factor, slack variable and kernel function parameters by immune algorithm was researched. The verification results of an engine's oil ferrography analysis data and adding noise data show that the method can effectively diagnose the aircraft engine wear fault and has good robustness. Finally, the impact of kernel function, multi-classification decision method, initial population size, affinity calculation formula, optimization algorithm and normalization method on diagnosis accuracy was analyzed, and the best algorithm was achieved. © 2017, Editorial Board of JBUAA. All right reserved.
ISSN No.:1001-5965
Translation or Not:no
Date of Publication:2017-07-01
Co-author:Li Yanjun,cyy,zln
Correspondence Author:刘长江
Date of Publication:2017-07-01