  Faults related to hydrodynamic bearing can imply in high maintenance costs
when late-detected and even to the total shutdown of the system. Thus,
techniques of early fault diagnosis have high relevance to the reliability of
rotating machinery. However, a common fault caused by inadequate bearing oil
supply has not yet received appropriate attention. This paper presents a new
approach to model and identify starved or excessive oil supply in hydrodynamic
bearings. The developed identification technique is a model-based process that
uses the rotor vibration signal to access the bearing lubrication. Numerical
identification tests were performed and the results showed that the proposed
method can satisfactorily estimate the oil flow rate in bearings under starved
and flooded lubrication conditions, thus representing a useful and promising
tool for condition monitoring and fault diagnosis applied to rotating
machinery.
