关键词: Kriging模型, KVPMCD, 滚动轴承, 故障诊断

Abstract:

The fault diagnosis of rolling bearings was a pattern recognition problem essentially, variable predictive model-based class discriminate(VPMCD) was a new pattern recognition method, in essence, through the inner relations among these features, a mathematic predication model that could be established and used to predict the features of those diagnosed bearings and thus to achieve the goal of pattern recognition. But the VPMCD classification method could only use the regression model to predict, so it would reduce the prediction accuracy when the relation of features was complicated. Aiming at this defect, a KVPMCD pattern recognition method was presented. Kriging model was composed of regression model and correlation model, and the latter was local deviation which was created on the basis of global model, and it could reveal the spatial correlation with features exactly, so as to make up for the shortcomings of simple regression model in the original VPMCD.The analysis results of the UCI standard data and the experimental data of rolling bearing show that compared to the original VPMCD, KVPMCD pattern recognition method can identify the working states and fault types of rolling bearings effectively.

Key words: Kriging model, Kriging variable predictive mode based class discriminate(KVPMCD), rolling bearing, fault diagnosis