Machinery condition prediction based on wavelet and support vector machine

The soft failure of mechanical equipment makes its performance drop gradually, which occupies a large proportion and has certain regularity. The performance can be evaluated and predicted through early state monitoring and data analysis. In this paper, the support vector machine (SVM), a novel learning machine based on the VC dimension theory of statistical learning theory, is described and applied in machinery condition prediction. To improve the modeling capability, wavelet transform (WT) is introduced into the SVM model to reduce the influence of irregular characteristics and simultaneously simplify the complexity of the original signal. The paper models the vibration signal from the double row bearing and wavelet transformation and SVM model (WT–SVM model) is constructed and trained for bearing degradation process prediction. Besides Hazen plotting position relationships is applied to describe the degradation trend distribution and a 95 % confidence level based on \(t\)-distribution is given. The single SVM model and neural network (NN) approach is also investigated as a comparison. The modeling results indicate that the WT–SVM model outperforms the NN and single SVM models, and is feasible and effective in machinery condition prediction.