Summary of Dual Adversarial and Contrastive Network For Single-source Domain Generalization in Fault Diagnosis, by Guangqiang Li et al.
Dual adversarial and contrastive network for single-source domain generalization in fault diagnosis
by Guangqiang Li, M. Amine Atoui, Xiangshun Li
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed dual adversarial and contrastive network (DACN) tackles the challenge of fault diagnosis in industrial systems, where limited fault data are often available from a single mode. By generating diverse sample features and extracting domain-invariant representations, DACN demonstrates high classification accuracy on unseen modes while maintaining a small model size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to diagnose faults in machines when we only have information about how the machine behaves in one situation. It’s like trying to predict what will happen in a different weather condition based on data from just one type of weather. The authors use a special kind of computer program called DACN that can generate fake test data and learn from it to improve its ability to diagnose faults in new situations. |
Keywords
* Artificial intelligence * Classification
