Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions
Muhammad Tanzil Furqon Mahardhika Pratama Lin Liu Habibullah Kutluyil Dogancay
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}.