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Bearing defects decision making using higher order spectra features and support vector machines

This paper presents a novel pattern classification approach for bearing defects diagnostics, which combine the higher order spectra (HOS) analysis features and support vector machine classifier (SVM). The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the bearing mechanical vibration signals. The vibration bi-spectrum patterns are extracted as the feature vectors presenting different faults of the bearings. The extracted bi-spectrum features are subjected to principal component analysis (PCA) for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing conditions namely: normal, inner race fault, outer race fault and ball fault, which were measured in the experimental test bench running under different working conditions. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on mechanical vibration signals.