Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions

Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions Muhammad Furqon, Mahardhika Pratama, Lin Liu, Habibullah Habibullah, Kutluyil Dogancay We propose mix-up domain adaptation for time-series unsupervised domain adaptation. MDAN is applied to dynamic remaining useful life predictions and fault diagnosis. We propose a self-supervised learning method via a controlled reconstruction learning. Abstract 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.