Summary of Mixup Domain Adaptations For Dynamic Remaining Useful Life Predictions, by Muhammad Tanzil Furqon et al.
Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions
by Muhammad Tanzil Furqon, Mahardhika Pratama, Lin Liu, Habibullah, Kutluyil Dogancay
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 mix-up domain adaptation (MDAN) method addresses the limitation of existing RUL prediction works by assuming identical conditions between training and deployment phases. MDAN consists of a three-stage mechanism that regularizes source and target domains using a mix-up strategy, creates an intermediate mix-up domain for alignment, and employs self-supervised learning to prevent supervision collapse. Compared to recent publications, MDAN outperforms its counterparts in 12 out of 12 cases for dynamic RUL predictions. Additionally, it surpasses prior art with significant margins in 8 out of 12 cases when evaluated on the bearing machine dataset. The proposed method is publicly available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps industries predict how long their assets will last before breaking down. This is important because it can help them reduce downtime and save money. Most current methods assume that the conditions in which they train their models are the same as those in which they will be used. However, this isn’t always true. The proposed mix-up domain adaptation method tries to solve this problem by creating a “mix-up” domain that combines training and deployment conditions. This approach is shown to be more effective than current methods in predicting remaining useful life. |
Keywords
* Artificial intelligence * Alignment * Domain adaptation * Self supervised
