Abstract: In order to recognize the damage types of aeroengine automatically and reliably,enhance the capability of aeroengine maintenance support,the feature extraction method based on color moments and gray level co-occurrence matrix (GLCM) is proposed to construct the feature database of the aeroengine's non-destructive detection images,and the support vector machine (SVM) is utilized as intelligent classifier for damages recognition.A dual mutation particles swarm optimization (DMPSO) algorithm is designed to optimize the kernel parameter and penalty factor for guaranteeing the recognition performance of SVM,dual mutation strategy improves the global optimization capability,and some complex test functions have been used to prove DMPSO'sperformance.Finally,the feature databases are constructed by different feature methods according to four damage types of certain aeroengine,and then the proposed SVM optimized by DMPSO is used for damage types recognition compared with back propagation (BP) network,extreme learning machine (ELM) network,and k-nearest neighborhood (k-NN).The recognition results have proven the proposed feature extraction method is more suitable for aeroengine damage recognition and is helpful to improve the accuracy of damage recognition.Meanwhile,the recognition performances of the four algorithms are compared,and the comparison results have demonstrated the optimized SVM always has better and stable recognition output.The comparison experiment has proven that the methods proposed in this paper are helpful to improve the recognition efficiency of aeroengine damage types.

Key words: Color moments, Damage types recognition, GLCM, PSO, SVM