diagnosis. This paper proposes a novel deep learning method for rolling bearing fault diag- nosis using multi-layer domain adaptation. As shown in Figure 1, the domain shift problem is expected to be solved by jointly minimizing the classification error and the distribution ...
In order to improve the fault diagnosis reliability, a new multisensor data fusion technique is proposed. First, time-domain and frequency-domain features are extracted from the different sensor signals, and then these features are input into multiple two-layer sparse autoencoder (SAE) neural ...
In order to improve the fault diagnosis reliability, a multidomain feature fusion for varying speed bearing diagnosis using broad learning system is proposed. First, a multidomain feature fusion is adopted to realize the unified form of vibration characteristics at different speeds. Time-domain and ...
In this paper, a novel infrared and visible image fusion strategy is developed based on a multi-layer fusion framework. First, the image is decomposed into multiple parts based on the low-rank decomposition theory. Second, a fusion strategy tailored to the unique feature of each modality is ...
CNN is mainly composed of convolutional layer, pooling layer, and activation layer. This paper will focus on 1-D CNN as the input is vibration signals. Data description As shown in Fig. 8, a bearing test rig has been set up to collect vibration signals to verify the effectiveness of the...
2025Multi-layer Multi-level Comprehensive Learning for Deep Multi-view Clustering3MCIF 2025Interpretable Multi-view Clustering-PR- 2025Deep Multi-view Clustering with Diverse and Discriminative Feature LearningDDMVCPR 2024Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense FrameworkAR...
(GBDT), RF, Self-Normalizing Neural-Networks (SNN), and Batch-NormalizedMLP. The highest accuracy of 83.42% has been achieved using the hidden-layer fusion method, which improves the accuracy of a uni-modal (either based upon EM or EEG) by 7.37%. So the proposed system is a significant...
Finally, the visual and textual data are fed into the convolutional neural network (CNN) and multi-layer perceptron (MLP) fusion model for predicting the mechanical properties of carbon fibers. The developed hybrid CNN-MLP fusion model achieves encouraging average testing R2 of longitudinal modulus,...
the Fusion Attention Network for Bearing Diagnosis (FAN-BD) achieves higher accuracy and robustness in fault diagnosis tasks, providing an efficient and reliable solution for bearing fault diagnosis.The proposed model outperforms ViT, Swin Transformer, ConvNeXt, and CBMA-ViT in terms of classification...
bearing; deep learning; fault diagnosis; multi-task learning; variable operating conditions; vibration imaging1. Introduction Rotating machinery has become faster and more intelligent in recent years due to rapid innovation, and plays an increasingly vital role in many industries [1,2]. With this ...