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- W2894906403 abstract "Aiming at reducing the classification accuracy caused by unbalanced sample data in bearing fault diagnosis, we present a bearing fault diagnosis method based on wavelet packet and fuzzy support vector machine (FSVM). In the actual sampling environment, the bearing fault sample is difficult to obtain, the data between the normal sample and the fault sample are not balanced, and the bearing is easily caused by the difference of the factors such as the personnel operation, the environment temperature, and the inherent noise of the machine. Therefore, the wavelet packet decomposition technique is used to extract the energy of the bearing vibration signal, and adaptive FSVM is used as a fault diagnosis algorithm to solve the unbalancing problem of training samples. The simulation results show that compared with the standard SVM and FSVM, the method can improve the classification accuracy of bearing fault diagnosis." @default.
- W2894906403 created "2018-10-12" @default.
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- W2894906403 date "2018-09-01" @default.
- W2894906403 modified "2023-09-26" @default.
- W2894906403 title "Research of Fault Diagnosis of Rolling Bearing Based on Wavelet Packet and FSVM" @default.
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- W2894906403 doi "https://doi.org/10.1088/1742-6596/1087/4/042061" @default.
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