Bearing Diagnostics Based on Pattern Recognition of Statistical Parameters

Abstract

In this paper, a new bearing defect diagnostic and classification method is proposed based on pattern recognition of statistical parameters. Such a pattern recognition problem can be described as transformation from the pattern space to the feature space and then to the classification space. Based on trend analysis of six commonly used statistical parameters, four parameters, namely, RMS, Kurtosis, Crest Factor, and Impulse Factor, are selected to form a pattern space. A 2-D feature space is formulated by a nonlinear transformation. An intraclass transformation is used to cluster the data of different bearing defects into different regions in the feature space. The classification space is constructed by piecewise linear discriminant functions. Training the classification space is performed, in this paper, by using data of bearings with seeded defects. Diagnosis of the defected bearings in the classification space then becomes straightforward. Numerical experiments show that the proposed method is effective in indicating both the location and the severity of bearing defects.