Application of a New VAE-MF Generative Model in TCD Dataset

Zhang, Xueying; Chen, Xiaoyu; Guo, Yi; Wang, Suzhe; Jia, Wei

doi:10.1109/icceai55464.2022.00032

2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) 2022

DOI: 10.1109/icceai55464.2022.00032

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Application of a New VAE-MF Generative Model in TCD Dataset

Xueying Zhang

Xiaoyu Chen

Yi Guo

et al.

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2024

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Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

Li,

Liu,

et al. 2024

Structural Health Monitoring

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For the poor model generalization and low diagnostic efficiency of fault diagnosis under imbalanced distributions, a novel fault diagnosis method using variational autoencoder generation adversarial network and improved convolutional neural network, named VGAIC-FDM, is proposed in this paper. First, to capture local features of vibration signals, continuous wavelet transform is employed to convert the original one-dimensional fault signals into wavelet time–frequency images. Second, for the data dimensionality reduction and model simplification, the time–frequency wavelet images are processed in grayscale to generate single-channel grayscale time–frequency images. Then, sample augmentation is performed on grayscale time–frequency images to balance the dataset by using a variational autoencoder generation adversarial network. Finally, the generated images and the original images are fused and trained by using a focus-loss-optimized CNN classifier to achieve fault diagnosis under unbalanced conditions. The experimental results show that the VGAIC-FDM effectively captures the potential spatial distribution of real samples and alleviates the impact caused by the inconsistent difficulty of sample classification. As a result, it enhances the fault diagnosis performance of the model when dealing with unbalanced datasets, leading to higher accuracy and F1-score values.

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Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

Li,

Liu,

et al. 2024

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

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Application of a New VAE-MF Generative Model in TCD Dataset

Cited by 1 publication

References 7 publications

Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

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