Work

Identification of non-circular profiles in hydrodynamic journal bearings

Year: 2024

Type: article

Abstract: 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.

Source: Mechanism and Machine Theory

DOI: 10.1016/j.mechmachtheory.2024.105819

Authors: Matheus Victor Inacio, Katia Lucchesi Cavalca, Gregory Bregion Daniel

Institution: Universidade Estadual de Campinas (UNICAMP)

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,740

Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico