Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions
This work addresses domain adaptation for RUL predictions, which is crucial for industries to reduce downtime and maintenance costs, but it appears incremental as it builds on existing mix-up and domain adaptation techniques.
The paper tackled the problem of predicting Remaining Useful Life (RUL) under non-i.i.d. conditions by proposing a mix-up domain adaptation method, which outperformed existing methods in 12 out of 12 cases for dynamic RUL predictions and in 8 out of 12 cases for a bearing machine dataset.
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}.