An approach for fault diagnosis based on an improved k-nearest neighbor algorithm
The k-nearest neighbor (k-NN) algorithm has received many attentions for its applications in fault diagnosis. However, the conventional k-NN algorithm often provides a misleading diagnosis due to slight data variation near the threshold. This paper presents an improved k-NN algorithm based on D-S evidence theory. D-S evidence theory is adopted to identify and linearize the edge data. A set-valued decision information system is established based on fault diagnosis rules and mass functions. The k-NN algorithm is used to classify the features in the decision information system. The simulation experiments of oil-immersed power transformer are performed with the conventional algorithm and the improved algorithm. The simulation result shows that the accuracy rate of the proposed method is higher than conventional method.