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Condition monitoring and prognostics of bearings considering uncertainties

Grant number: 19/00974-1
Support Opportunities:Regular Research Grants
Duration: June 01, 2019 - August 31, 2022
Field of knowledge:Engineering - Mechanical Engineering - Mechanical Engineering Design
Convênio/Acordo: Texas Tech University
Mobility Program: SPRINT - Projetos de pesquisa - Mobilidade
Principal Investigator:Katia Lucchesi Cavalca Dedini
Grantee:Katia Lucchesi Cavalca Dedini
Principal researcher abroad: Stephen Ekwaro-Osire
Institution abroad: Texas Tech University (TTU), United States
Host Institution: Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated researchers:Gregory Bregion Daniel ; Helio Fiori de Castro ; João Paulo Dias ; Ozhan Gecgel ; Tiago Henrique Machado
Associated research grant:15/20363-6 - Fault tolerant identification and control of rotating systems, AP.TEM


Rotating systems represent a class of machines with great application in industry. Among the main component of rotating systems, bearing elements are the most susceptible to faults. Many techniques that have been developed for condition monitoring and prognostics of bearings use model-based responses. In this context, the development of robust and representative models for each component of the rotating system becomes crucial, attending the new tendencies demanded by Industry 4.0. Furthermore, faults in bearing are impacted by considerable stochastic fluctuation of the machine operation conditions and the material properties. Therefore, it is also imperative to consider probabilistic and uncertainty quantification approaches in order to develop robust condition monitoring and prognostics strategies of bearing elements. From these motivations, the research question proposed for this work is: Can condition monitoring and prognostics strategies of bearings be improved by using dynamic modeling and uncertainty quantification approaches? In order to respond the research question, three objectives were developed, namely, (1) to develop realistic mathematical models to describe the most common fault mechanisms and the impact on dynamic response of bearings elements, (2) to develop a robust probabilistic framework to account for the many sources of uncertainty on the bearing operational parameters and material properties, and (3) to develop advanced strategies of condition monitoring and prognostics of bearings elements. (AU)

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Scientific publications
(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)
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)
GARPELLI, LUCAS NOGUEIRA; ALVES, DIOGO STUANI; CAVALCA, KATIA LUCCHESI; DE CASTRO, HELIO FIORI. Physics-guided neural networks applied in rotor unbalance problems. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, v. N/A, p. 14-pg., . (19/00974-1)
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)

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