A Siamese Vision Transformer for Bearings Fault Diagnosis

He, Qiuchen; Li, Shaobo; Bai, Qiang; Zhang, Ansi; Yang, Jing; Shen, Mingming

doi:10.3390/mi13101656

Cited by 14 publications

(5 citation statements)

References 55 publications

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“…This design addresses the high computational costs due to the self-attention scheme's quadratic complexity, thereby significantly enhancing fault classification efficiency. To address the limitations of Transformer application in fault diagnosis due to factors such as limited training data and complex working environments, He et al [23] proposed an intelligent bearing fault diagnosis method that combines Siamese networks with Vision Transformer (VIT). This approach effectively completes fault diagnosis by extracting feature vectors of input samples in a higher-dimensional spatial domain using VIT.…”

Section: Introductionmentioning

confidence: 99%

Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

Gao,

Wang,

et al. 2024

Meas. Sci. Technol.

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Current self-attention based Transformer models in the field of fault diagnosis are limited to identifying correlation information within a single sequence and are unable to capture both time and frequency domain fault characteristics of the original signal. To address these limitations, this research introduces a two-channel Transformer fault diagnosis model that integrates time and frequency domain features through a cross-attention mechanism. Initially, the original time-domain fault signal is converted to the frequency domain using the Fast Fourier Transform, followed by global and local feature extraction via a Convolutional Neural Network. Next, through the self-attention mechanism on the two-channel Transformer, separate fault features associated with long distances within each sequence are modeled and then fed into the feature fusion module of the cross-attention mechanism. During the fusion process, frequency domain features serve as the query sequence Q, and time domain features as the key-value pairs K. By calculating the attention weights between Q and K, the model excavates deeper fault features of the original signal. Besides preserving the intrinsic associative information within sequences learned via the self-attention mechanism, the Twins Transformer also models the degree of association between different sequence features using the cross-attention mechanism. Finally, the proposed model's performance was validated using four different experiments on four bearing datasets, achieving average accuracy rates of 99.67%, 98.76%, 98.47%, and 99.41%. These results confirm the model's effective extraction of time and frequency domain correlation features, demonstrating fast convergence, superior performance, and high accuracy.

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Section: Introductionmentioning

confidence: 99%

Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

Gao,

Wang,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Tang et al [35] combined integrated learning and visual Transformer for the diagnosis of bearing fault through wavelet transform to split the vibration signal as multiple sub-signals, with each of these sub-signals as input to the transformer. He et al [36] combined Siamese networks and transformer to improve the performance in the diagnosis of bearing fault under the context of cross-domain tasks and noisy environments.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis Method of Rolling Bearings Combining Convolutional Neural Network and Transformer

Liu

Zhang

et al. 2023

Electronics

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Efficient and accurate fault diagnosis plays an essential role in the safe operation of machinery. In respect of fault diagnosis, various data-driven methods based on deep learning have attracted widespread attention for research in recent years. Considering the limitations of feature representation in convolutional structures for fault diagnosis, and the demanding requirements on the quality of data for Transformer structures, an intelligent method of fault diagnosis is proposed in the present study for bearings, namely Efficient Convolutional Transformer (ECTN). Firstly, the time-frequency representation is achieved by means of short-time Fourier transform for the original signal. Secondly, the low-level local features are extracted using an efficient convolution module. Then, the global information is extracted through transformer. Finally, the results of fault diagnosis are obtained by the classifier. Moreover, experiments are conducted on two different bearing datasets to obtain the experimental results showing that the proposed method is effective in combining the advantages of CNN and transformer. In comparison with other single-structure methods of fault diagnosis, the method proposed in this study produces a better diagnostic performance in the context of limited data volume, strong noise, and variable operating conditions.

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“…Wang et al [10] improved the diagnostic task by utilizing the time-shifted coarse-grained construction technique and a refined computing technique to improve the traditional multiscale fuzzy entropy for feature extraction, while supervised isometric mapping is used to reduce the feature dimensions, and finally, adaptive chaotic Aquila-optimized SVM is used for fault classification. Most of the above methods require manual feature selection, which requires considerable expertise and extensive mathematical knowledge when analyzing complex systems [11]. Hence, their diagnostic results are highly uncertain and poorly generalized.…”

Section: Introductionmentioning

confidence: 99%

Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion

Yu,

Fu,

Song

et al. 2023

Meas. Sci. Technol.

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Current deep-learning methods are often based on significantly large quantities of labeled fault data for supervised training. In practice, it is difficult to obtain samples of rolling bearing failures. In this paper, a transfer learning-based feature fusion convolutional neural network approach for bearing fault diagnosis is proposed. Specifically, the raw vibration signal features and the corresponding time-frequency image features of the input data are extracted by a one-dimensional convolutional neural network and a pre-trained ConvNeXt, respectively, and connected by a feature fusion strategy. Then, the fine-tuning method based on transfer learning can effectively reduce the reliance on labeled samples in the target domain. A wide convolution kernel is introduced in the time-domain signal feature extraction to increase the receptive field, which is combined with the channel attention mechanism to further optimize the feature quality. Finally, two common bearing datasets are utilized for fault diagnosis experiments. The experimental results show that the proposed model achieves an average accuracy of more than 98.63% in both cross-working conditions and cross-device diagnosis tasks. Meanwhile, anti-noise experiments and ablation experiments further validate the accuracy and robustness of the proposed method.

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A Siamese Vision Transformer for Bearings Fault Diagnosis

Cited by 14 publications

References 55 publications

Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

A Novel Fault Diagnosis Method of Rolling Bearings Combining Convolutional Neural Network and Transformer

Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion

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