Application on Damage Types Recognition of Civil Aeroengine Based on SVM Optimized by DMPSO

Abstract In order to recognize the damage types of aeroengine automatically and Reliably, enhance the support capability of aeroengine maintenance, 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 damage images. The support vector machine (SVM) is utilized as intelligent classifier for damages recognition. Meanwhile, a double-mutations particles swarm optimization (DMPSO) algorithm is designed to optimize the kernel parameter and penalty factor for guaranteeing the recognition performance of SVM. Finally, the feature databases are constructed by different feature methods according to actual four damage types of a certain aeroengine, and then the proposed SVM optimized by DMPSO is used to compare 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. Meanwhile, the comparison results have demonstrated the optimized SVM always has better and stable recognition output.