Machinery Fault Diagnosis Using Least Squares Support Vector Machine

Authors:
Lingling Zhao;Kuihe Yang
Affiliations:
College of Information, Hebei University of Science and Technology, Shijiazhuang 050054, China;College of Information, Hebei University of Science and Technology, Shijiazhuang 050054, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

In order to enhance fault diagnosis precision, an improved fault diagnosis model based on least squares support vector machine (LSSVM) is presented. In the model, the wavelet packet analysis and LSSVM are combined effectively. The power spectrum of fault signals are decomposed by wavelet packet analysis, which predigests choosing method of fault eigenvectors. And then the LSSVM is adopted to realize fault diagnosis. The non-sensitive loss function is replaced by quadratic loss function and the inequality constraints are replaced by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. It is presented to choose parameter of kernel function in definite range by dynamic way, which enhances preciseness rate of recognition. The simulation results show the model has strong non-linear solution and anti-jamming ability, and it can effectively distinguish fault type.