| Grant number: | 17/18629-3 |
| Support Opportunities: | Research Grants - Visiting Researcher Grant - International |
| Start date: | December 04, 2017 |
| End date: | January 10, 2018 |
| Field of knowledge: | Engineering - Mechanical Engineering - Mechanics of Solids |
| Principal Investigator: | Katia Lucchesi Cavalca Dedini |
| Grantee: | Katia Lucchesi Cavalca Dedini |
| Visiting researcher: | Ilmar Ferreira Santos |
| Visiting researcher institution: | Technical University of Denmark (DTU) , Denmark |
| Host Institution: | Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| City of the host institution: | Campinas |
| Associated research grant: | 15/20363-6 - Fault tolerant identification and control of rotating systems, AP.TEM |
Abstract
This project is focused on unbalance and bearing wear fault diagnosis in rotating machinery using model based identification techniques. Since this technique requires accurate models, the hydrodynamic nonlinear forces of the bearings are considered in the finite element model of the shaft. The bearing forces are obtained by the solution of the Reynolds' equation in time domain. The solution of rotor dynamic problems in time domain requires the solution of the lubrication model for each time step, and consequently, presents high computational cost, so the force characterization as function dependent on the shaft rotation, position and velocity become important in the numerical modeling context. Other factor to be concerned with, therefore, is the vibration from the nonlinearities, or subsequent harmonics, and their influence on the shaft orbit inside the bearings. Finally, considering system and fault models (mass unbalance or bearing wear), it is necessary to proceed with the fault identification, i.e., to estimate the characteristic faults parameters. An objective function, therefore, takes into account the difference between the vibration amplitude originated by the faulty machine (experimental procedure) and by the fault mathematical model, and the fault parameters can be obtained by a search algorithm that minimizes the objective function. (AU)
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