A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

Liu, Caiming; Zheng, Xiaorong; Bao, Zhengyi; He, Zhiwei; Gao, Mingyu; Song, Wenlong

doi:10.3390/e24081087

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…Efficient channel attention requires only a few parameters to produce remarkable results [42]. Due to the superior performance of ECA-Net, many studies have been conducted to use it for the adaptation of channel feature weights [43,44]. However, the ECA-Net input is mainly two-dimensional features, and this study improves its input structure for channel weight adaptation of one-dimensional features.…”

Section: Eca-netmentioning

confidence: 99%

“…The experiments were all performed under the CWRU dataset A. The number of convolutional kernels from large to small was established, and the effect of using a larger number of convolutional kernels was compared according to the study of Liu et al [44]. The number of channels and the size of the convolutional kernel are initially set for common convolutional kernel sizes.…”

Section: Influence Of Wide Filters and Convolution Channel Sizementioning

confidence: 99%

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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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Section: Eca-netmentioning

confidence: 99%

Section: Influence Of Wide Filters and Convolution Channel Sizementioning

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.

View full text Add to dashboard Cite

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Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network

Wang,

Sun,

Zhu

et al. 2023

MBE

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<abstract> <p>As an essential component of mechanical equipment, the fault diagnosis of rolling bearings may not only guarantee the systematic operation of the equipment, but also minimize any financial losses caused by equipment shutdowns. Fault diagnosis algorithms based on convolutional neural networks (CNN) have been widely used. However, traditional CNNs have limited feature representation capabilities, thereby making it challenging to determine their hyperparameters. This paper proposes a fault diagnosis method that combines a 1D-CNN with an attention mechanism and hyperparameter optimization to overcome the aforementioned limitations; this method improves the search speed for optimal hyperparameters of CNN models, improves the diagnostic accuracy, and enhances the representation of fault feature information in CNNs. First, the 1D-CNN is improved by combining it with an attention mechanism to enhance the fault feature information. Second, a swarm intelligence algorithm based on Differential Evolution (DE) and Grey Wolf Optimization (GWO) is proposed, which not only improves the convergence accuracy, but also increases the search efficiency. Finally, the improved 1D-CNN alongside hyperparameters optimization are used to diagnose the faults of rolling bearings. By using the Case Western Reserve University (CWRU) and Jiangnan University (JNU) datasets, when compared to other common diagnosis models, the results demonstrate the usefulness and dependability of the DE-GWO-CNN algorithm in fault diagnosis applications by demonstrating the increased diagnostic accuracy and superior anti-noise capabilities of the proposed method. The fault diagnosis methodology presented in this paper can accurately identify faults and provide dependable fault classification, thereby assisting technicians in promptly resolving faults and minimizing equipment failures and operational instabilities.</p> </abstract>

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A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

Cited by 2 publications

References 40 publications

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

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

Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network

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