SRMANet: Toward an Interpretable Neural Network with Multi-Attention Mechanism for Gearbox Fault Diagnosis

Liu, Siyuan; Huang, Jinying; Ma, Jiancheng; Luo, Jia

doi:10.3390/app12168388

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…Step 2: Mixed sampling method for unbalanced data. To address the issue of category imbalance in the dataset, a hybrid sampling method that combines oversampling and random undersampling is employed [31]. For fault samples belonging to minority categories, oversampling is employed to augment their data, ensuring comprehensive feature learning by the model.…”

Section: Power Battery Fault Diagnosis Modelmentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

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The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

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Section: Power Battery Fault Diagnosis Modelmentioning

confidence: 99%

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Zhou,

Zhang,

Wang

2024

Processes

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“…When the input value is negative, the non zero activation value is output, which improves the zero-gradient problem of the negative half of ReLU and makes the network respond to both positive and negative directions. The PReLU function is well adapted to the reciprocal fluctuation nature of vibration signals and reduces information loss [23]. The expression of the PReLU activation function is as follows:…”

Section: Feature Extraction Modulementioning

confidence: 99%

Multi-View Information Fusion Fault Diagnosis Method Based on Attention Mechanism and Convolutional Neural Network

Jin-ying

Gao

et al. 2022

Applied Sciences

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Multi-view information fusion can provide more accurate, complete and reliable data descriptions for monitoring objects, effectively improve the limitations and unreliability of single-view data. Existing multi-view information fusion based on deep learning mostly focuses on the feature level and decision level, with large information loss, and does not distinguish the view weight in the fusion process. To this end, a multi-view data level information fusion model CAM_MCFCNN with view weight was proposed based on a channel attention mechanism and convolutional neural network. The model used the channel characteristics to implement multi-view information fusion at the data level stage, which made the fusion position and mode more natural and reduced the loss of information. A multi-channel fusion convolutional neural network was used for feature learning. In addition, the channel attention mechanism was used to learn the view weight, so that the algorithm could pay more attention to the views that contribute more to the fault identification task during the training process, and more reasonably integrate the information of different views. The proposed method was verified by the data of the planetary gearbox experimental platform. The multi-view data and single-view data were used as the input of the CAM_MCFCNN model and single-channel CNN model respectively for comparison. The average accuracy of CAM_MCFCNN on three constant-speed datasets reached 99.95%, 99.87% and 99.92%, which was an improvement of 0.95%, 2.25%, and 0.04%, compared with the single view with the highest diagnostic accuracy, respectively. When facing limited samples, CAM_MCFCNN had similar performance. Finally, compared with different multi-view information fusion algorithms, CAM_MCFCNN showed better stability and higher accuracy. The experimental results showed that the proposed method had better performance, higher diagnostic accuracy and was more reliable, compared with other methods.

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“…Li et al 21 combined Dual-stage Attention-based Recurrent Neural Network (DA-RNN) and Convolutional Block Attention Module (CBAM) to obtain a bearing fault diagnosis model, which achieved good diagnosis results under unbalanced data condition. Liu et al 22 constructed a stacked residual multi-attention network (SRMANet) to take critical feature components of gearbox vibration signals. Zhao et al 23 presented a novel rotor system fault diagnosis model based on parallel convolutional neural network architecture with attention mechanism (AMPCNN), which had good performance for load adaptability and noise immunity.…”

Section: Introductionmentioning

confidence: 99%

Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning

Cheng,

Dou,

et al. 2024

Sci Rep

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In practical engineering, the working conditions of gearbox are complex and variable. In varying working conditions, the performance of intelligent fault diagnosis model is degraded because of limited valid samples and large data distribution differences of gearbox signals. Based on these issues, this research proposes a gearbox fault diagnosis method integrated with lightweight channel attention mechanism, and further realizes the cross-component transfer learning. First, time–frequency distribution of original signals is obtained by wavelet transform. It could intuitively reflect local characteristics of signals. Secondly, based on a local cross-channel interaction strategy, a lightweight efficient channel attention mechanism (LECA) is designed. The kernel size of 1D convolution is affected by channel number and coefficients. Multi-scale feature input is used to retain more detailed features of different dimensions. A lightweight convolutional neural network is constructed. Finally, a transfer learning method is applied to freeze lower structures of the network and fine-tune higher structures of the model using small samples. Through experimental verification, the proposed model could effectively utilize samples. The application of transfer learning could realize accurate and fast fault classification of small samples, and achieve good gearbox fault diagnosis effect under varying working conditions and cross-component conditions.

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SRMANet: Toward an Interpretable Neural Network with Multi-Attention Mechanism for Gearbox Fault Diagnosis

Cited by 6 publications

References 42 publications

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Fault Diagnosis for Power Batteries Based on a Stacked Sparse Autoencoder and a Convolutional Block Attention Capsule Network

Multi-View Information Fusion Fault Diagnosis Method Based on Attention Mechanism and Convolutional Neural Network

Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning

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