Gear Fault Diagnosis with Support Vector Machine

Jia Li Tang; Chen Rong Huang; Jian Min Zuo

doi:10.4028/www.scientific.net/AMR.455-456.1169

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 455-456Gear Fault Diagnosis with Support Vector Machine

Gear Fault Diagnosis with Support Vector Machine

Abstract:

Because of the complexity of gear working condition, there are non-linear relationship between characteristic parameters and fault types. This paper proposes to apply the Support Vector Machine to set up the nonlinear mapping to solve the difficulties of gear fault diagnosis. Taking a certain gearbox fault signal acquisition experimental system for instance, Matlab software and its neural network toolbox are used to model and simulate. The simulation result shows the founded model has preferable learning and generalization capabilities, which performs effectively in the common gear fault diagnosis and it can identify various types of faults stably and accurately.

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Periodical:

Advanced Materials Research (Volumes 455-456)

Pages:

1169-1174

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.455-456.1169

Citation:

Cite this paper

Online since:

January 2012

Authors:

Jia Li Tang, Chen Rong Huang, Jian Min Zuo

Keywords:

Gear Fault Diagnosis, Neural Network (NN), Support Vector Machine (SVM)

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