Authors: Huawang Shi
Addresses: School of Civil Engineering, Hebei University of Engineering, Handan, China
Abstract: In this paper, we present an application of support vector machines (SVMs) and particle swarm optimisation (PSO) to fault diagnosis. SVMs have been successfully employed to solve regression problem of nonlinearity and small sample. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. PSO is a new optimisation method, which is motivated by social behaviour of organisms such as bird flocking and fish schooling. The method not only has strong global search capability but also is very easy to implement. Thus, the proposed PSO-SVM model is applied to diagnosis operation of rolling bearing failure in this paper, in which PSO is used to determine free parameters of SVM. The experimental results also indicate that the SVM method can achieve greater accuracy than grey model, artificial neural network under the condition of availability of small training data.
Keywords: SVMs; support vector machines; PSO; particle swarm optimisation; fault diagnosis; PSO-SVM; intelligent diagnosis.
International Journal of Computer Applications in Technology, 2012 Vol.44 No.2, pp.159 - 164
Available online: 20 Aug 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article