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• W2019502814 abstract "Fault diagnosis is very important to ensure the safe operation of hydraulic generator units (HGU). Because of the complexity of HGU, the vast amounts of measured data and the redundant information, the accuracy and instantaneity of fault diagnosis are severely limited. At present, feature selection technique is an effective method to break through this bottleneck. According to the specific characteristics of HGU faults, this paper puts forward a hierarchical feature selection method based on classification tree (HFSMCT). HFSMCT selects the most effective feature for each branch node through filtering evaluation criteria and heuristic search strategy, and all the selected features constitute the final feature set. Moreover, HFSMCT is easy to design and implement, and it is very prominent in computational efficiency and accuracy. The simulation results also prove that HFSMCT is very suitable for HGU fault diagnosis." @default.
• W2019502814 created "2016-06-24" @default.
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• W2019502814 date "2014-10-01" @default.
• W2019502814 modified "2023-09-27" @default.
• W2019502814 title "A Hierarchical Feature Selection Method Based on Classification Tree for HGU Fault Diagnosis" @default.
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• W2019502814 doi "https://doi.org/10.4028/www.scientific.net/amr.1037.398" @default.
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