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Experimental evaluation of a fault model for wear in hydrodynamic bearings.

Grant number: 18/21581-5
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): November 01, 2018
Effective date (End): May 31, 2020
Field of knowledge:Engineering - Mechanical Engineering - Mechanics of Solids
Principal researcher:Katia Lucchesi Cavalca Dedini
Grantee:Diogo Stuani Alves
Home Institution: Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:15/20363-6 - Fault tolerant identification and control of rotating systems, AP.TEM
Associated scholarship(s):18/24600-0 - Experimental evaluation of a fault model for wear in hydrodynamic bearings, BE.EP.PD

Abstract

This project is related to the section 4.1.3 of the FAPESP thematic project grant #2015/20363-6 and consists of a temporal response analysis of a rotor-bearing system under worn bearings condition, since these components significantly influence the dynamic response of rotors. In addition to the numerical results obtained by a proposed model, it will be evaluated the experimental dynamic response of the corresponding rotating system. It will be used an instrumented test rig so that the system time response can be studied by observing the sensitivity of this response to the wear parameters. It is expected to detect the rise of additional harmonics, as observed by Chasalevris et al. 2013. To achieve this goal, an external excitation is given, in addition to the mass unbalance, by an electromagnetic actuator, facilitating the variation of the excitation frequency, and enabling the application of forces without direct contact with the rotor. In the next stage, the aim is to validate the proposed numerical model for the worn bearing with experimental results and, consequently, obtain a robust model that is sensitive to the wear parameters. Finally, it is proposed a methodology for identifying the presence of wear in journal bearings, using information from harmonics that emerges in the frequency spectrum of the rotating system response.

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Scientific publications (7)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
ALVES, DIOGO STUANI; DANIEL, GREGORY BREGION; DE CASTRO, HELIO FIORI; MACHADO, TIAGO HENRIQUE; CAVALCA, KATIA LUCCHESI; GECGEL, OZHAN; DIAS, JOAO PAULO; EKWARO-OSIRE, STEPHEN. Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault. MECHANISM AND MACHINE THEORY, v. 149, . (19/00974-1, 15/20363-6, 18/21581-5)
GAROLI, GABRIEL YUJI; ALVES, DIOGO STUANI; MACHADO, TIAGO HENRIQUE; CAVALCA, KATIA LUCCHESI; DE CASTRO, HELIO FIORI. Fault parameter identification in rotating system: Comparison between deterministic and stochastic approaches. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, v. 20, n. 6, . (16/13223-6, 15/20363-6, 18/21581-5, 18/24600-0)
ALVES, DIOGO STUANI; MACHADO, TIAGO HENRIQUE; CAVALCA, KATIA LUCCHESI; BACHSCHMID, NICOLO. Characteristics of oil film nonlinearity in bearings and its effects in rotor balancing. Journal of Sound and Vibration, v. 459, . (15/20363-6, 18/01919-1, 18/21581-5)
ALVES, DIOGO STUANI; LUCCHESI, KATIA. Investigation into the influence of bearings nonlinear forces in unbalance identification. Journal of Sound and Vibration, v. 492, . (18/21581-5, 15/20363-6)
RIBEIRO, EDUARDO AFONSO; ALVES, DIOGO STUANI; CAVALCA, KATIA LUCCHESI; BAVASTRI, CARLOS ALBERTO. Stability analysis and optimization of a hybrid rotating machinery support combining journal bearings with viscoelastic supports. MECHANISM AND MACHINE THEORY, v. 156, . (18/21581-5, 15/20363-6)
ALVES, DIOGO STUANI; FIEUX, GAUTHIER; MACHADO, TIAGO HENRIQUE; KEOGH, PATRICK S.; CAVALCA, KATIA LUCCHESI. A parametric model to identify hydrodynamic bearing wear at a single rotating speed. TRIBOLOGY INTERNATIONAL, v. 153, . (15/20363-6, 18/21581-5, 18/24600-0)
GECGEL, OZHAN; DIAS, JOAO PAULO; EKWARO-OSIRE, STEPHEN; ALVES, DIOGO STUANI; MACHADO, TIAGO HENRIQUE; DANIEL, GREGORY BREGION; DE CASTRO, HELIO FIORI; CAVALCA, KATIA LUCCHESI. Simulation-Driven Deep Learning Approach for Wear Diagnostics in Hydrodynamic Journal Bearings. JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME, v. 143, n. 8, . (15/20363-6, 18/21581-5, 19/00974-1)

Please report errors in scientific publications list by writing to: cdi@fapesp.br.