  Vibrations of rotating machinery primarily originate from two sources, both
of which are distorted by the machine's transfer function on their way to the
sensor: the dominant gear-related vibrations and a low-energy signal linked to
bearing faults. The proposed method facilitates the blind separation of
vibration sources, eliminating the need for any information about the monitored
equipment or external measurements. This method estimates both sources in two
stages: initially, the gear signal is isolated using a dilated CNN, followed by
the estimation of the bearing fault signal using the squared log envelope of
the residual. The effect of the transfer function is removed from both sources
using a novel whitening-based deconvolution method (WBD). Both simulation and
experimental results demonstrate the method's ability to detect bearing
failures early when no additional information is available. This study
considers both local and distributed bearing faults, assuming that the
vibrations are recorded under stable operating conditions.
