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 \url{https://github.com/furqon3009/MDAN}.

翻译：剩余寿命预测在资产规划与维护中发挥关键作用，可为企业带来减少停机时间、降低维护成本等诸多益处。尽管已有大量研究致力于该课题，但现有工作大多局限于独立同分布条件，假设训练阶段与部署阶段的数据分布相同。本文针对该问题提出混合域适应（MDAN）解决方案。MDAN采用三阶段机制，其混合策略不仅用于对源域和目标域进行正则化，还通过构建中间混合域实现两域的对齐。同时引入自监督学习策略以预防监督坍塌问题。通过与近期动态剩余寿命预测研究成果的严格对比评估，MDAN在全部12个测试案例中均以显著优势超越基准方法。此外，在轴承机械数据集上的实验表明，MDAN在12个案例中有8个案例的表现大幅领先现有最优方法。MDAN的源代码已公开发布于\url{https://github.com/furqon3009/MDAN}。