A model-based technique to identify lubrication condition of hydrodynamic bearings using the rotor vibrational response

TL;DR

This paper introduces a model-based method that uses rotor vibration signals to accurately identify lubrication conditions in hydrodynamic bearings, aiding early fault detection and reducing maintenance costs.

Contribution

A novel model-based approach for diagnosing starved or excessive oil supply in hydrodynamic bearings using vibration analysis, addressing a previously overlooked fault condition.

Findings

Successfully estimated oil flow rate under various lubrication conditions

Demonstrated effectiveness in early fault detection

Provides a promising tool for condition monitoring

Abstract

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.