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- W4323568082 abstract "Axis-orbit recognition is an essential means for the fault diagnosis of hydropower units. An axis-orbit recognition method based on feature combination and feature selection is proposed, aiming to solve the problems of the low recognition accuracy, poor robustness, and low efficiency of existing axis-orbit recognition methods. First, various contour, moment, and geometric features of axis orbit samples are extracted from the original data and combined into a multidimensional feature set; then, Random Forest (RF)-Fisher feature selection is applied to realize feature dimensionality reduction; and finally, the selected features are set as the input of the support vector machine (SVM), which is optimized by the gravitational search algorithm (GSA) for axis-orbit recognition. The analytical results show that the proposed method has high recognition efficiency and good robustness while maintaining high accuracy for axis-orbit recognition." @default.
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- W4323568082 date "2023-03-07" @default.
- W4323568082 modified "2023-09-26" @default.
- W4323568082 title "Axis Orbit Recognition of the Hydropower Unit Based on Feature Combination and Feature Selection" @default.
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- W4323568082 doi "https://doi.org/10.3390/s23062895" @default.
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