{"title":"Identification of non-circular profiles in hydrodynamic journal bearings","authors":"","doi":"10.1016/j.mechmachtheory.2024.105819","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49845,"journal":{"name":"Mechanism and Machine Theory","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanism and Machine Theory","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0094114X24002465","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Mechanism and Machine Theory provides a medium of communication between engineers and scientists engaged in research and development within the fields of knowledge embraced by IFToMM, the International Federation for the Promotion of Mechanism and Machine Science, therefore affiliated with IFToMM as its official research journal.
The main topics are:
Design Theory and Methodology;
Haptics and Human-Machine-Interfaces;
Robotics, Mechatronics and Micro-Machines;
Mechanisms, Mechanical Transmissions and Machines;
Kinematics, Dynamics, and Control of Mechanical Systems;
Applications to Bioengineering and Molecular Chemistry