ArticleScientific reports2024
A motor bearing fault diagnosis model based on multi-adversarial domain adaptation.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Reliability-Guided Adaptive Feature Fusion Network for Noise-Robust Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- Disentangle-and-aggregate feature learning (DAFNet) for motor bearing fault diagnosis.Scientific reports · 2026Article
- Advanced deep learning approach for the fault severity classification of rolling-element bearings.Scientific reports · 2025Article
- Unpaired image to image translation for source free domain adaptation in semantic segmentation.Scientific reports · 2025Article
- Multimodal data generative fusion method for complex system health condition estimation.Scientific reports · 2025Article
- A hybrid approach combining deep learning and signal processing for bearing fault diagnosis under imbalanced samples and multiple operating conditions.Scientific reports · 2025Article
- Integrating vibration signal analysis and image embedding for enhanced bearing fault diagnosis in manufacturing.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In response to the weakened capability of feature transfer and parameter distribution alignment across domains due to significant differences in data distribution collected by different devices, this paper constructs a motor bearing fault diagnosis model based on multi-adversarial domain adaptation. Initially, an improved residual network is employed as the feature extraction module to enhance feature extraction capabilities. It then incorporates a Selective Kernel Network (SKNet) to implement attention mechanisms on convolutional kernels, and a Global Context Network (GCNet) to effectively utilize global contextual information for re-weighting across different channels. Additionally, the model uses a multi-kernel maximum mean discrepancy to measure the distribution between domains and classes, establishing a dynamic adjustment factor in conjunction with multiple domain discriminators to modulate the importance of marginal and conditional distributions. Ultimately, the proposed model was applied to transfer experiments across different operating conditions and devices, demonstrating excellent diagnostic results.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.