Identification of oil starvation in hydrodynamic journal bearing using rotor vibration and Extended Kalman Filter

Oil starvation is a critical fault in hydrodynamic bearings caused by insufficient oil supply. When late detected, this fault can deteriorate the bearings’ performance and damage the rotating machine. Thus, the fault should be identified early to allow preventive maintenance. However, the literature about oil starvation identification is scarce. Among the few studies available, incipient oil starvation fault has been identified using a deterministic technique, which can be jeopardized by model inaccuracy. This paper proposes a novel identification method using the Extended Kalman Filter, which is a stochastic-determinist estimator that deals with modeling and measurement errors. Simulations showed the proposed method is suitable to identify oil starvation in real-time from noisy vibration, representing a promising tool for monitoring rotating machines.