  It has been shown that ultrasonic techniques work well for online measuring
of circumferential oil film thickness profile in journal bearings;
unfortunately, they can be limited by their measuring range and unable to
capture details of the film all around the bearing circumference. Attempts to
model the film thickness over the full range of the bearing rely on
deterministic approaches, which assume the observations to be true with
absolute certainty. Unaccounted uncertainties of the film thickness may lead to
a cascade of inaccurate predictions for subsequent calculations of hydrodynamic
parameters. In the present work, a probabilistic framework is proposed to model
the film thickness with Gaussian Processes. The results are then used to
estimate the location of the bearing shaft under various operational
conditions. A further step in the process involves using the newly-constructed
dataset to generate likelihood maps displaying the probable location of the
shaft centre, given the bearing rotational speed and applied static load. The
results offer the possibility to visualise the confidence of the predictions
and allow the true location to be found within an area of high probability
within the bearing's bore.
