# Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions

arXiv: https://arxiv.org/abs/2404.04824 · HTML: https://mlsanity.com/paper/2404.04824/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Correlation Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1612.01939/) (2016)
- [Open Domain Generalization with Domain-Augmented Meta-Learning](https://mlsanity.com/paper/2104.03620/) (2021)
- [Dual Mixup Regularized Learning for Adversarial Domain Adaptation](https://mlsanity.com/paper/2007.03141/) (2020)
- [Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective](https://mlsanity.com/paper/2303.13434/) (2023)
- [Remaining Useful Lifetime Prediction via Deep Domain Adaptation](https://mlsanity.com/paper/1907.07480/) (2019)
- [Self-supervised Autoregressive Domain Adaptation for Time Series Data](https://mlsanity.com/paper/2111.14834/) (2021)
- [MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning](https://mlsanity.com/paper/1908.10059/) (2019)
- [MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds](https://mlsanity.com/paper/2304.01554/) (2023)

## Built on by (most cited first, 4 of 4)

- [Time and Frequency Synergy for Source-Free Time-Series Domain Adaptations](https://mlsanity.com/paper/2410.17511/) (2024)
- [Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends](https://mlsanity.com/paper/2510.03604/) (2025)
- [Black-Box Time-Series Domain Adaptation via Cross-Prompt Foundation Models](https://mlsanity.com/paper/2510.00487/) (2025)
- [Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model](https://mlsanity.com/paper/2601.22581/) (2026)

## Abstract (arXiv)

Remaining Useful Life (RUL) predictions play vital role for asset planning and maintenance leading to many benefits to industries such as reduced downtime, low maintenance costs, etc. Although various efforts have been devoted to study this topic, most existing works are restricted for i.i.d conditions assuming the same condition of the training phase and the deployment phase. This paper proposes a solution to this problem where a mix-up domain adaptation (MDAN) is put forward. MDAN encompasses a three-staged mechanism where the mix-up strategy is not only performed to regularize the source and target domains but also applied to establish an intermediate mix-up domain where the source and target domains are aligned. The self-supervised learning strategy is implemented to prevent the supervision collapse problem. Rigorous evaluations have been performed where MDAN is compared to recently published works for dynamic RUL predictions. MDAN outperforms its counterparts with substantial margins in 12 out of 12 cases. In addition, MDAN is evaluated with the bearing machine dataset where it beats prior art with significant gaps in 8 of 12 cases. Source codes of MDAN are made publicly available in urlhttps://github.com/furqon3009/MDAN.