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
https://github.com/furqon3009/MDAN.

该论文介绍了一种使用混合域适应和自监督学习策略的解决方案，用于预测剩余使用寿命（Remaining Useful Life，RUL），在 12 个案例中，在动态 RUL 预测方面，该解决方案优于其他最近发表的作品，其中 8 个案例中，它也优于先前的技术。

混合领域适应用于动态剩余寿命预测

Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions

Operating units often experience various failure modes in complex systems,
leading to distinct degradation paths. Relying on a prognostic model trained on
a single failure mode may lead to poor generalization performance across
multiple failure modes. Therefore, accurately identifying the failure mode is
of critical importance. Current prognostic approaches either ignore failure
modes during degradation or assume known failure mode labels, which can be
challenging to acquire in practice. Moreover, the high dimensionality and
complex relations of sensor signals make it challenging to identify the failure
modes accurately. To address these issues, we propose a novel failure mode
diagnosis method that leverages a dimension reduction technique called UMAP
(Uniform Manifold Approximation and Projection) to project and visualize each
unit's degradation trajectory into a lower dimension. Then, using these
degradation trajectories, we develop a time series-based clustering method to
identify the training units' failure modes. Finally, we introduce a
monotonically constrained prognostic model to predict the failure mode labels
and RUL of the test units simultaneously using the obtained failure modes of
the training units. The proposed prognostic model provides failure
mode-specific RUL predictions while preserving the monotonic property of the
RUL predictions across consecutive time steps. We evaluate the proposed model
using a case study with the aircraft gas turbine engine dataset.

该研究提出了一种基于降维技术 UMAP 的故障模式诊断方法，通过将每个单元的退化轨迹投影和可视化到低维空间，并利用这些退化轨迹开发基于时间序列的聚类方法来识别训练单元的故障模式。最后，引入一种具有单调约束的预测模型，能够同时预测测试单元的故障模式标签和剩余寿命，并保持剩余寿命预测值在连续时间步长中的单调性。该模型通过飞机燃气涡轮发动机数据集进行了验证。