Rolling element bearing fault detection using support vector machine with improved ant colony optimization

被引：92

作者：

Li, Xu ^{[1
]}

Zheng, A'nan ^{[2
]}

Zhang, Xunan ^{[3
]}

Li, Chenchen ^{[2
]}

Zhang, Li ^{[2
]}

机构：

[1] Northeastern Univ, State Key Lab Rolling & Automat, Shenyang 110004, Peoples R China

[2] Liaoning Univ, Sch Informat, Shenyang 110036, Peoples R China

[3] Tsinghua Univ, Beijing 100084, Peoples R China

来源：

MEASUREMENT | 2013年 / 46卷 / 08期

关键词：

Fault detection; Rolling element bearing; Ant colony optimization; Support vector machine; Meshing; DIAGNOSIS; EXTRACTION; ALGORITHM;

D O I：

10.1016/j.measurement.2013.04.081

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In support vector machine (SVM), it is quite necessary to optimize the parameters which are the key factors impacting the classification performance. Improved ant colony optimization (IACO) algorithm is proposed to determine the parameters, and then the IACO-SVM algorithm is applied on the rolling element bearing fault detection. Both the optimal and the worst solutions found by the ants are allowed to update the pheromone trail density, and the mesh is applied in the ACO to adjust the range of optimized parameters. The experimental data of rolling bearing vibration signal is used to illustrate the performance of IACO-SVM algorithm by comparing with the parameters in SVM optimized by genetic algorithm (GA), cross-validation and standard ACO methods. The experimental results show that the proposed algorithm of IACO-SVM can give higher recognition accuracy. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：2726 / 2734

页数：9

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