Rolling Bearing Fault Diagnosis and Prediction Method Based on Gray Support Vector Machine Model

Abstract

The article put forward a method based on GM (1, 1)-SVM for rolling bearing fault, prediction and diagnosis. Firstly, the method extract, time and frequency domain feature values, of vibration, signal of rolling bearing under, all kinds of, fault, and, normal condition. Then the method select important characteristic, parameters to build a grey model and carry on multi, step prediction, Lastly, the method use all kinds of fault and normal condition eigen value to train binary tree support vector machine and construct the decision tree of rolling bearing to classify the, fault type.

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Cite this paper

@article{Wang2015RollingBF, title={Rolling Bearing Fault Diagnosis and Prediction Method Based on Gray Support Vector Machine Model}, author={Jianhua Wang and Taiti Kang}, journal={2015 International Conference on Computer Science and Mechanical Automation (CSMA)}, year={2015}, pages={313-317} }