Abstract. Machine learning techniques are indispensable in today's data-driven fault diagnosis methodolgoies. Among many machine techniques, knearest 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.

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