Identification of non-circular profiles in hydrodynamic journal bearings

Hydrodynamic bearings, especially cylindrical radial plain journal bearings, are widely utilized in industry for their high load capacity and low friction energy losses. However, these bearings are prone to faults such as wear and ovalization, which can deform their circular profile and affect their vibrational response. Detecting these faults is essential to reduce their impact on production. This study introduces a methodology to identify hydrodynamic bearings with non-circular profiles. The bearing model and its numerical solution are implemented using the Finite Volume Method, with the effects of failures incorporated into a rotating system modeled by the Finite Element Method. A dataset is generated to reflect three common failure conditions in industrial applications: wear, ovalization, and a combination of ovalization with wear. The authors used this dataset to train a Multilayer Perceptron (MLP) neural network, which can identify the bearing profile shape based on specific attributes of the dynamic responses. The identification tests for the three fault conditions demonstrated high accuracy, particularly in distinguishing between ovalization and wear.