  Bearing is a key component in industrial machinery and its failure may lead
to unwanted downtime and economic loss. Hence, it is necessary to predict the
remaining useful life (RUL) of bearings. Conventional data-driven approaches of
RUL prediction require expert domain knowledge for manual feature extraction
and may suffer from data distribution discrepancy between training and test
data. In this study, we propose a novel generalized multiscale feature
extraction method with generative adversarial networks. The adversarial
training learns the distribution of training data from different bearings and
is introduced for health stage division and RUL prediction. To capture the
sequence feature from a one-dimensional vibration signal, we adapt a U-Net
architecture that reconstructs features to process them with multiscale layers
in the generator of the adversarial network. To validate the proposed method,
comprehensive experiments on two rotating machinery datasets have been
conducted to predict the RUL. The experimental results show that the proposed
feature extraction method can effectively predict the RUL and outperforms the
conventional RUL prediction approaches based on deep neural networks. The
implementation code is available at https://github.com/opensuh/GMFE.
