Springer International Publishing Switzerland 2016, DOI: 10.1007/978-3-319-40973-3_25

A Sequential k-Nearest Neighbor Classification Approach for Data-Driven Fault Diagnosis Using Distance- and Density-Based Affinity Measures

Myeongsu Kang a, Gopala Krishnan Ramaswami b, Melinda Hodkiewiczc, Edward Cripps d, Jong-Myon Kim e, Michael Pecht a

a Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA
b Department of Physics, National University of Singapore, Singapore, Singapore
c School of Mechanical and Chemical Engineering, University of Western Australia, Crawley, WA, Australia
d School of Mathematics and Statistics, University of Western Australia, Crawley, WA, Australia
e Department of IT Convergence, University of Ulsan, Ulsan, South Korea


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.

Keywords: Data-driven fault diagnosis, Density-based affinity measure, k-Nearest neighbor, Machine learning

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