Rolling Bearing Fault Diagnosis Based on SVD-GST Combined with Vision Transformer

Xie, Fengyun; Wang, Gan; Zhu, Haiyan; Sun, Enguang; Fan, Qiuyang; Wang, Yang

doi:10.3390/electronics12163515

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…To evaluate the superiority of the proposed method, a noise immunity comparison test was carried out, and five methods were selected to compare the performance, including SVD-GST [33], 1D-ViTMSC [25], BiGRV [34], ResNet18 [35] and ViT network. Using the same experimental parameter settings, the adaptability of the fault diagnosis method under different noise levels is evaluated by adding different degrees of Gaussian white noise to the vibration signal.…”

Section: Anti-noise Testmentioning

confidence: 99%

Rolling bearing fault diagnosis method based on PE-DCM and ViT

Hui,

Xu,

Chen

et al. 2024

Meas. Sci. Technol.

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Considering the issue of capturing the local and global contextual information and enhancing the parallel capability of bearing fault diagnosis in variable load and noise environments, a fault diagnosis method of rolling bearing based on PE-DCM and Vision Transformer (ViT) is proposed. Firstly, the one-dimensional vibration signal is converted into a two-dimensional time-frequency diagram by continuous wavelet transform in the data processing module, and the model can understand the characteristics of the vibration signal more comprehensively. Secondly, a pyramid exponential expansion convolution module is established to extract the local features of fault information. Then, the global features of the fault information are learned through the ViT network, and the adaptive multi-attention is used to dynamically adjust the attention weights according to the features of the input data so as to inhibit noise or unimportant information. Finally, the experimental verification is carried out by using Case Western Reserve University and self-made MFS-bearing data set. The experimental results show that the method can better reflect the powerful image classification ability of the ViT network and has better noise resistance and generalization compared with other fault diagnosis methods.

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Section: Anti-noise Testmentioning

confidence: 99%

Rolling bearing fault diagnosis method based on PE-DCM and ViT

Hui,

Xu,

Chen

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…[35], suggests using CNN and the optimal Hilbert curve (OHC) approach for bearing fault identification. [36], transforms one-dimensional vibration signals into two-dimensional timefrequency pictures using the generalized S-transform (GST).Although these approaches are capable of extracting features from vibration signals and detecting faults, they still have the following drawbacks when used to diagnose bearing faults. First, their feature extraction capabilities must be enhanced.…”

Section: Introductionmentioning

confidence: 99%

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

She,

Ai,

et al. 2024

Eng. Res. Express

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Bearing fault diagnosis is of great significance to the normal operation of machinery, and its performance and life span directly affect the operational efficiency and safety of the whole equipment. For existing image coding methods which detecting the bearing fault with a large number of training samples and complex neural networks to achieve the desired detection performance, the Multiscale Permutation Entropy Gray Image Coding (MPEGIC) method is proposed. In order to fully extract the feature information of the time series signal, this paper uses the Multiscale Permutation Entropy (MPE) method to construct a new image coding method by calculating the alignment information of the time series to reflect the complexity and randomness of the time series at different scales, and dividing the obtained feature matrix and mapping it to the gray-scale image domain. And it is experimentally verified by Case Western Reserve University (CWRU) bearing dataset and self-made rotor experimental platform bearing dataset. The results show that the method in this paper effectively reduces the number of training samples and the number of model parameters, and maintains a better detection performance even in a strong noise background.

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“…With the continuous development of artificial intelligence technology, deep learning (DL) has been introduced into the field of bearing fault diagnosis by more and more scholars [8], such as convolutional neural network (CNN) [9], recurrent neural network [10], graph neural network [11], and other networks have achieved remarkable success through their intelligent processing of large amounts of data, automatic extraction and learning of discriminative features, and high discrimination. However, DL relies on many labeled and co-distributed data during model training, which is unachievable in real industrial scenarios.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis of double-aligned domain adaptation based on multi-structure fusion and multi-attention mechanism

Shang,

Wu,

Pan

et al. 2024

Meas. Sci. Technol.

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Currently, most fault diagnosis methods based on domain adaptation reduce the distribution discrepancy between the source and target domains by aligning their high-dimensional features, but do not consider the impact of the source domain decision boundary on the distribution of the target domain, which leads to slow convergence and poor generalization. Aiming at the above-mentioned issues, this paper proposes a double-aligned domain adaptation deep transfer model (MSADA) based on multi-structure fusion and multi-attention mechanisms. Firstly, a multi-structure fusion network with Inception module is constructed to increase the information capacity in the extracted features and enhance the representation of deep features. Then, the multi-attention mechanism is embedded into different convolutional channels separately to learn globally and locally important information and reduce the influence of the source domain decision boundary on the target domain distribution. Finally, the multi-kernel maximum mean discrepancy (MK-MMD) and KL divergence are combined as a new double-aligned distribution discrepancy metric to align the samples and probability distributions of the source domain and the target domain, respectively. At the same time, a dynamic adaptive factor is designed to adjust the contribution of the two types of distributions, thus effectively improving the training efficiency and the robustness of the model. Through the validation analysis of two rolling bearing dataset cases, the proposed MSADA has better cross-domain diagnostic performance than other domain adaptation methods.

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Rolling Bearing Fault Diagnosis Based on SVD-GST Combined with Vision Transformer

Cited by 9 publications

References 32 publications

Rolling bearing fault diagnosis method based on PE-DCM and ViT

Rolling bearing fault diagnosis method based on PE-DCM and ViT

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

Intelligent fault diagnosis of double-aligned domain adaptation based on multi-structure fusion and multi-attention mechanism

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