Anzeige
Erschienen in:
2017 | OriginalPaper | Buchkapitel
Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN
verfasst von : Dileep Kumar Appana, Md. Rashedul Islam, Jong-Myon Kim
Erschienen in: Artificial Life and Computational Intelligence
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
The k-nearest neighbor (k-NN) method is a simple and highly effective classifier, but the classification accuracy of k-NN is degraded and becomes highly sensitive to the neighborhood size k in multi-classification problems, where the density of data samples varies across different classes. This is mainly due to the method using only a distance-based measure of similarity between different samples. In this paper, we propose a density-weighted distance similarity metric, which considers the relative densities of samples in addition to the distances between samples to improve the classification accuracy of standard k-NN. The performance of the proposed k-NN approach is not affected by the neighborhood size k. Experimental results show that the proposed approach yields better classification accuracy than traditional k-NN for fault diagnosis of rolling element bearings.