  Despite the recent success in data-driven fault diagnosis of rotating
machines, there are still remaining challenges in this field. Among the issues
to be addressed, is the lack of information about variety of faults the system
may encounter in the field. In this paper, we assume a partial knowledge of the
system faults and use the corresponding data to train a convolutional neural
network. A combination of t-SNE method and clustering techniques is then
employed to detect novel faults. Upon detection, the network is augmented using
the new data. Finally, a test setup is used to validate this two-stage
methodology on a centrifugal pump and experimental results show high accuracy
in detecting novel faults.
