ArticleScientific reports2024
A domain generalization network for imbalanced machinery fault diagnosis.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Bearing Single-Source Domain Generalization Fault Diagnosis Method Based on Adaptive Frequency-Domain Augmentation and Unsupervised Contrastive Learning.Sensors (Basel, Switzerland) · 2026Article
- IoT-Driven Robust Bearing Fault Diagnosis for Induction Motors Under Operating-Condition Shift.Sensors (Basel, Switzerland) · 2026Article
- Cross domain fault diagnosis in internal combustion engines using multisensor data with transfer federated and transformer based federated transfer learning.Scientific reports · 2025Article
- Experimental performance examination of a coherence technique-based numerical differential current relay for AC machine stator windings protection.Scientific reports · 2025Article
- Transformer-based conditional generative transfer learning network for cross domain fault diagnosis under limited data.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
Traditional models for Imbalanced Fault Diagnosis (IFD) face challenges in practical applications due to domain shifts caused by varying working conditions and machinery. Domain Generalization (DG) models provide an advantage over traditional approaches by learning class-discriminative and domain-invariant feature representations, allowing them to generalize to unseen target data. However, the scarcity of fault samples relative to healthy ones limits their application in real-world industrial scenarios. In this paper, we propose a Domain Mixed-Enhanced Domain Generalization Network (DEMDGN) that enhances IFD performance by utilizing mixup-based data augmentation and domain-based discrepancy metrics to align feature distributions across multiple heterogeneous source domains. By creating domain-invariant features, DEMDGN allows robust fault diagnosis under varying conditions. Extensive experiments on one marine machinery dataset and two bearing datasets demonstrate that the proposed method effectively addresses class imbalance and domain shift problems, achieving superior diagnostic performance.
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