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- W4360996933 abstract "The new power system has put forward higher requirements for the reliability of power supply. As the electrical energy conversion device, the research on the fault diagnosis method of the motor provides strong support for the reliability of the power supply. In this paper, the diagnosis of motor-bearing fault types is studied. The frequency domain extraction and EMD information entropy method are used to extract the characteristics of motor fault data. Then, the decision tree diagnosis model is trained and tested by using the feature data set. The experimental results show that the diagnostic accuracy rate of using only the frequency domain feature extraction data and decision tree is 85.67%, the macro F1 score is 0.8537, and the micro F1 score is 0.8567. In contrast, when the frequency domain feature and EMD information entropy feature are introduced into decision tree diagnosis at the same time. The accuracy of diagnosis can reach 93.29%, the macro F1 score reaches 0.9327, and the micro F1 score reaches 0.9329, which provides a new idea for studying motor fault diagnosis methods." @default.
- W4360996933 created "2023-03-30" @default.
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- W4360996933 date "2022-10-01" @default.
- W4360996933 modified "2023-10-14" @default.
- W4360996933 title "Fault diagnosis method of motor bearing based on EMD information entropy and decision tree" @default.
- W4360996933 doi "https://doi.org/10.1109/aiam57466.2022.00138" @default.
- W4360996933 hasPublicationYear "2022" @default.
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