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- W4366420746 abstract "To achieve multi-mode fault sample generation and fault diagnosis of bearings in a complex operating environment with scarce labeled data. Combining a semi-supervised generative adversarial network (SGAN) and an auxiliary classifier generative adversarial network (ACGAN), a semi-supervised auxiliary classifier generative adversarial network (SACGAN) is constructed in this paper. The network structure and the loss function are improved. A fault diagnosis method based on STFT-SACGAN is also proposed. The method uses a short-time Fourier transform (STFT) to convert one-dimensional time-domain vibration signals of bearings into two-dimensional time-frequency images, which are used as the input of SACGAN. Two multi-mode fault data generation and intelligent diagnosis cases for bearings are studied. The experimental results show that the proposed method generates high-quality multi-mode fault samples with high fault diagnosis accuracy, generalization, and stability." @default.
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- W4366420746 date "2023-04-18" @default.
- W4366420746 modified "2023-09-30" @default.
- W4366420746 title "Multi-Mode Data Generation and Fault Diagnosis of Bearings Based on STFT-SACGAN" @default.
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- W4366420746 doi "https://doi.org/10.3390/electronics12081910" @default.
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