ArticleSensors (Basel, Switzerland)2025
Domain Adversarial Transfer Learning Bearing Fault Diagnosis Model Incorporating Structural Adjustment Modules.
Article in Sensors (Basel, Switzerland), 2025. 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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5 citing papers in PubMed.
- An End-to-End Fault Diagnosis Model for Rolling Bearings Based on Multi-Scale Convolution and the Kolmogorov-Arnold Network.Sensors (Basel, Switzerland) · 2026Article
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Dynamic Vision-Based Non-Contact Rotating Machine Fault Diagnosis with EViT.Sensors (Basel, Switzerland) · 2025Article
- A Review of Artificial Intelligence Techniques in Fault Diagnosis of Electric Machines.Sensors (Basel, Switzerland) · 2025Review
- Bearing Fault Diagnosis Based on Time-Frequency Dual Domains and Feature Fusion of ResNet-CACNN-BiGRU-SDPA.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
Funding
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Abstract
With the improvement in industrial equipment intelligence and reliability requirements, bearing fault diagnosis has become a key technology to ensure the stable operation of mechanical equipment. Traditional bearing fault diagnosis methods are ineffective in diagnosing complex faults and mostly rely on the manual adjustment of hyperparameters. To this end, this paper proposes a domain adversarial migratory learning bearing fault diagnosis model incorporating structural adjustment modules. First, the pre-trained model of the source domain is applied to the target domain dataset through an adversarial domain adaptation technique. Then, the network depth and width are dynamically adjusted in the Optuna optimization framework to accommodate more complex fault types in the target domain. Finally, the performance of the model is further improved by automatically optimizing the hyperparameters. The experimental results show that the model exhibits high accuracy in the diagnosis of different fault types, especially in the face of complex and variable industrial environments, demonstrating strong adaptability and robustness. The method provides an effective solution for fault diagnosis of intelligent devices.
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