Fault Diagnosis of Rotating Mechanism Based on Ant Colony SVDD Algorithm and Cluster Method

Abstract

Abstract: For the absence of typical fault samples, the general machine learning methods can not be used directly. A hybrid fault diagnosis scheme for rotating mechanism was proposed combining the SVDD algorithm with the Davies Bouldin index (DBI) K-cluster method. Firstly, the SVDD model is constructed for the samples in the normal condition, and the ant colony algorithm is utilized to optimize the SVDD parameters. Then, when the number of rejected samples reaches a given threshold, the K-cluster method is employed to classify these samples and the labels are obtained; furthermore, the number of the classification is determined in accordance with the DBI. Finally, the one class samples are trained with SVDD individually, and the SVDD classifiers are joined to a complete diagnosis model based on a binary tree structure. For the multifault mode samples of rolling element bearing, the speed of training is nearly 10 times greater than that of the grid search approach, while the DBI is verified to determine the number of clusters, and the recognition rate for the bearing samples reaches 100%.

Key words: ant colony support vector date description (SVDD), Kcluster method, Davies Bouldin index, rotating mechanism, fault diagnosis

DU Wen-Liao-1, 2 , LI An-Sheng-2, SUN Wang-1, LI Yan-Ming-1, LIU Cheng-Liang-1. Fault Diagnosis of Rotating Mechanism Based on Ant Colony SVDD Algorithm and Cluster Method[J]. Journal of Shanghai Jiaotong University, 2012, 46(09): 1440-1444.

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