Intelligent rotating machinery fault diagnosis based on super-resolution enhancement using data augmentation under large speed fluctuation

Wang, Xiaoyu; Han, Baokun; Lü, Tao; Zhang, Guowei; Wang, Jinrui

doi:10.1088/1361-6501/ac1edd

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…The study also utilized Kullback-Leibler Divergence (KLD) [37] and Pearson Correlation Coefficient (PCC) [37] to evaluate the resemblance among the produced samples and the original high-resolution samples. A smaller KLD value indicates greater similarity, while a larger PCC value suggests a stronger correlation.…”

Section: Comparison Methodmentioning

confidence: 99%

A novel signal resolution enhance method based on CBAM-ResNet for bearing intelligent fault diagnosis

Bao,

Qin,

Wan

et al. 2024

Meas. Sci. Technol.

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In health condition monitoring of mechanical equipment, the signal is the key source of information. However, signal resolution is often degraded due to factors such as equipment vibration and electromagnetic interference. To address this issue, an Efficient Sub-pixel Convolutional Attention Residual Network (ESPCARN) built on the idea of signal resolution improvement is proposed in this paper. Firstly, the original low-resolution samples are input into a CBAM-ResNet to obtain more feature information of the channels and space within the residual connection and a multi-feature mapping with four channels was generated. Subsequently, the four-channel low-resolution features are aligned periodically through sub-pixel convolution layer, resulting in a set of high-resolution samples and the feature dimension of the data was increased to four times that of the original low-resolution data, thereby realizing the resolution enhancement. Finally, two experiments with different working conditions are established to evaluate the performance of the proposed fault diagnosis method, and the experimental results verified the efficacy of the ESPCARN framework.

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Section: Comparison Methodmentioning

confidence: 99%

A novel signal resolution enhance method based on CBAM-ResNet for bearing intelligent fault diagnosis

Bao,

Qin,

Wan

et al. 2024

Meas. Sci. Technol.

Self Cite

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“…u k can fully reflect the shock characteristics of each channel. Finally, use the Softmax function according to equation (11) to calculate the weights of each channel,…”

Section: Wmcdf Strategy Based On Spectral Meanmentioning

confidence: 99%

“…Jia et al [10] inputted the data after Fourier transforms into the stacked autoencoder to realize efficient fault diagnosis, and demonstrated the feasibility of this method through bearing and gear datasets. Wang et al [11] solved the problem of decreased performance of the fault diagnosis model due to low input signal resolution from the perspective of enhancing signal resolution. Gao et al [12] achieved a good denoising effect on vibration signals by enhancing complementary ensemble empirical mode decomposition and utilized a one-dimensional convolutional neural network (1DCNN) to achieve efficient and accurate diagnosis of bearing faults.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis based on the fusion of sparse filtering and discriminative domain adaptation method under multi-channel data-driven

Jiao,

Zhang,

et al. 2024

Meas. Sci. Technol.

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Currently, the diagnostic performance of many deep learning algorithms may drop dramatically when the distribution of training data is significantly different from that of the test data. Moreover, the fault diagnosis approaches based on single-channel data may suffer problems such as large precision fluctuation, low reliability, and incomplete expression of fault features. To overcome the above deficiencies, a novel multi-channel data-driven fault recognition method based on the fusion of sparse filtering and discriminative domain adaptation (MSFDDA) is proposed in this article. Firstly, inspired by attention mechanisms and information fusion methods, a spectrum-based weighted multi-channel data fusion strategy (WMCDF) is designed to fully utilize the data collected by sensors to obtain a more comprehensive representation of fault features. Then, the joint probability-based discriminative maximum mean discrepancy algorithm is introduced into the sparse filtering method to strengthen the capability of extracting the domain invariant features. Finally, two bearing datasets are employed to verify the validity of the MSFDDA method, which proved to be superior to other current domain adaptation methods.

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“…Data augmentation methods use downsampling to augment the data set. Wang [6] et al periodically arranged multi-channel LR features through sub-pixel convolution layers to obtain a set of ultra-high-resolution features, which increased the data points by 16 times compared with the original data. The data generation method uses a generative adversarial network (GAN) to generate new data [7] .…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis method based on improved DenseNet with small samples

Song,

Liu,

et al. 2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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Solving the small sample problem can not only save data acquisition costs, but also enhance the model's generalization ability to unknown data, thereby ensuring the accuracy and reliability of fault diagnosis results. Therefore, this paper proposes a small-sample bearing fault diagnosis method that combines the ECA attention mechanism and the DenseNet network (ECA-DenseNet). First, the two-dimensional DenseNet network and ECA attention mechanism are converted into one-dimensional networks to reduce computational complexity and information loss. Secondly, the size and position of the convolutional layer in the network are improved and adjusted, and the 1D-ECA module is introduced into the DenseNet model. Finally, we use the original signal as the input of ECA-DenseNet. After feature recognition, the network classifier completes fault feature classification. We verified the effect of the proposed model under different working conditions on the laboratory PT500 bearing data set, and compared it with other diagnostic models. Experimental results show that the proposed method has high fault identification accuracy and generalization ability under small sample conditions.

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Intelligent rotating machinery fault diagnosis based on super-resolution enhancement using data augmentation under large speed fluctuation

Cited by 6 publications

References 25 publications

A novel signal resolution enhance method based on CBAM-ResNet for bearing intelligent fault diagnosis

A novel signal resolution enhance method based on CBAM-ResNet for bearing intelligent fault diagnosis

Rolling bearing fault diagnosis based on the fusion of sparse filtering and discriminative domain adaptation method under multi-channel data-driven

Bearing fault diagnosis method based on improved DenseNet with small samples

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