A sequential k-nearest neighbor classification approach for data-driven fault diagnosis using distance- and density-based affinity measures

Abstract

Machine learning techniques are indispensable in today’s data-driven fault diagnosis methodolgoies. Among many machine techniques, k-nearest neighbor (k-NN) is one of the most widely used for fault diagnosis due to its simplicity, effectiveness, and computational efficiency. However, the lack of a density-based affinity measure in the conventional k-NN algorithm can decrease the classification accuracy. To address this issue, a sequential k-NN classification methodology using distance- and density-based affinity measures in a sequential manner is introduced for classification.