ArticleSensors (Basel, Switzerland)2024
A Deep Learning-Based Method for Bearing Fault Diagnosis with Few-Shot Learning.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A CNN Feature Extraction and BKA-Optimized LSSVM Classification Method for Small-Sample Rolling Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- An End-to-End Fault Diagnosis Model for Rolling Bearings Based on Multi-Scale Convolution and the Kolmogorov-Arnold Network.Sensors (Basel, Switzerland) · 2026Article
- Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds.Scientific reports · 2026Article
- Dual-branch spatio-temporal graph network for bearing fault diagnosis.Scientific reports · 2026Article
- SA-ConSinGAN and reservoir computing fusion for accurate bearing fault classification and severity identification using GAF-based techniques.Scientific reports · 2026Article
- Rolling Bearing Fault Diagnosis Model Based on Multi-Scale Depthwise Separable Convolutional Neural Network Integrated with Spatial Attention Mechanism.Sensors (Basel, Switzerland) · 2025Article
- A poisson flow-based data augmentation and lightweight diagnosis framework for imbalanced rolling bearing faults.PloS one · 2025Article
- Study on Few-Shot Fault Diagnosis Method for Marine Fuel Systems Based on DT-SViT-KNN.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
Funding
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Abstract
To tackle the issue of limited sample data in small sample fault diagnosis for rolling bearings using deep learning, we propose a fault diagnosis method that integrates a KANs-CNN network. Initially, the raw vibration signals are converted into two-dimensional time-frequency images via a continuous wavelet transform. Next, Using CNN combined with KANs for feature extraction, the nonlinear activation of KANs helps extract deep and complex features from the data. After the output of CNN-KANs, an FAN network module is added. The FAN module can employ various feature aggregation strategies, such as weighted averaging, max pooling, addition aggregation, etc., to combine information from multiple feature levels. To further tackle the small sample issue, data generation is performed on the original data through diffusion networks under conditions of fewer samples for bearings and tools, thereby increasing the sample size of the dataset and enhancing fault diagnosis accuracy. Experimental results demonstrate that, under small sample conditions, this method achieves higher accuracy compared to other approaches.
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