A Weighted kNN Fault Detection Based on Multistep Index and Dynamic Neighborhood Scale Under Complex Working Conditions
نویسندگان
چکیده
Fault detection based on $k$ -nearest neighbor (FD- NN) is one of the most widespread fault techniques for industrial processes under complex working conditions, owing to its characteristic local modeling. However, state separation ability tends worsen when operating data heterogeneous distribution. To tackle this challenge, a weighted method multistep index and dynamic scale proposed. The nearest defined evaluate ability, framework formed by assigned weights obtained from kernel principal component analysis. On basis above, neighborhood correction threshold setting strategy are proposed deal with distribution track abrupt change operation state. 10 common faults wind turbines conditions used verify effectiveness method.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3272001