  Oil starvation is a critical fault in hydrodynamic bearings caused by
insufficient oil supply flowrate. When late detected, this fault can
deteriorate the bearings' performance and damage the rotating machine. Thus,
early fault identification techniques should be applied to avoid this scenario
and allow effective maintenance. However, the literature about the
identification of oil starvation fault is relatively recent and very scarce.
Among the few studies available, oil starvation has been identified in early
stages using a purely deterministic technique, which can be jeopardized by
model inaccuracy. Alternatively, this paper proposes a novel method for the
identification of oil starvation faults in hydrodynamic bearings using the
rotor vibrational responses and the Extended Kalman Filter (EKF), which is a
stochastic-determinist state estimator that can deal with modelling and
measurement errors. Computational simulations performed in this paper show that
the proposed method is suitable to successfully identify the oil starvation
faults in real-time even with noisy vibrational responses, thus representing a
promising tool for the monitoring of rotating machines.
