Imbalanced fault classification of rolling bearing based on an improved oversampling method

Han, Yanfang; Huang, Yingkun; Li, Liang; Yan, Kang

doi:10.1007/s40430-023-04142-9

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…In data-level methods, data augmentation can be categorized into two main types: simple and generative oversampling. Simple oversampling techniques include random sampling [22,23], overlapping sampling [24], and synthetic minority oversampling technique (SMOTE) [25][26][27][28]. These techniques generate additional data by sampling existing data.…”

Section: Introductionmentioning

confidence: 99%

Generation of high-dimensional vibration signal and its application in fault diagnosis

Ren,

Gao,

Zhu

et al. 2023

Meas. Sci. Technol.

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Imperfect data, such as data scarcity and imbalance, have a negative impact on intelligent fault diagnosis. Generative adversarial networks (GANs) have proven to be a potential method for augmenting data. However, the overall quality of data generated by GANs still requires improvement. In this study, a multiscale feature-fusion GAN was proposed for generating high-dimensional signals. A strategy that generates signals with dimensionality higher than the desired dimensionality was proposed. The generated signals were resampled to improve the phase diversity of the generated signals. To enhance the feature extraction capability of the GAN for high-dimensional signals, a multiscale feature extraction structure was designed. The integration of multiscale feature extraction and fusion was achieved without a significant increase in the computational burden. To enhance the amplitude diversity of the generated signals, a reconstruction network was designed that directly constrained the spatial distribution of the generated signals. Experimental results show that the model has advantages in terms of the similarity and diversity of the generated signals. The effectiveness of the model in fault diagnosis was verified using two motor datasets, where the fault diagnosis model using the sample set augmented by the proposed model obtained a 36.32% improvement in accuracy compared with using the original sample set. In addition, in comparison experiments, the model achieved a higher diagnostic accuracy improvement of 28.87% compared to four other published models.

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Section: Introductionmentioning

confidence: 99%

Generation of high-dimensional vibration signal and its application in fault diagnosis

Ren,

Gao,

Zhu

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention

Luo,

Yang,

Kang

et al. 2024

Actuators

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Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

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Imbalanced fault classification of rolling bearing based on an improved oversampling method

Cited by 2 publications

References 24 publications

Generation of high-dimensional vibration signal and its application in fault diagnosis

Generation of high-dimensional vibration signal and its application in fault diagnosis

Wind Turbine Bearing Failure Diagnosis Using Multi-Scale Feature Extraction and Residual Neural Networks with Block Attention

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