Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings

Abstract

An enhanced k-nearest neighbor (k-NN) classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditional k-NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size, k. This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposed k-NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE) signals. Experimental results demonstrate that the proposed scheme, which uses the enhanced k-NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size, k.