Banca de QUALIFICAÇÃO: MARCO ANTONIO LEANDRO CABRAL

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
DISCENTE : MARCO ANTONIO LEANDRO CABRAL
DATA : 13/12/2016
HORA: 09:30
LOCAL: Sala 9 da Escola de Ciências e Tecnologia
TÍTULO:

MONITORING CONTACT FAILURES IN AN AIR COMPRESSOR THROUGH AN UNSUPERVISED AUTOMATED SYSTEM WITH THE USE OF ARTIFICIAL NEURAL NETWORKS - SOM (SELF ORGANIZED MAPS) APPLIED TO PREDICTIVE MAINTENANCE IN ELECTROMECHANICAL PROCESSES

PALAVRAS-CHAVES:

Tribology, Electromechanical Systems, Maintenance, Signal Analysis, Artificial Neural Networks, FMEA, Reliability.

PÁGINAS: 115
GRANDE ÁREA: Engenharias
ÁREA: Engenharia Mecânica
SUBÁREA: Processos de Fabricação
ESPECIALIDADE: Robotização
RESUMO:

Preventing, anticipating and avoiding failures in electromechanical systems are demands that have challenged researchers and engineering professionals for decades. Electromechanical systems present tribological processes that result in fatigue of materials and consequent loss of efficiency or even usefulness of machines and equipment. Several techniques are used in an attempt to minimize the inherent losses of these systems through the analysis of signals from the equipment studied and the consequences of these wastes at unexpected moments, such as an aircraft in flight or a drilling rig in an oil well . Among these failure modes, we can mention vibration analysis, acoustic pressure measurement, temperature monitoring, debris analysis of lubricating oil etc. However, electromechanical systems are complex and may exhibit unexpected behavior. Reliability centered maintenance requires ever faster, more efficient and robust technological resources to ensure its efficiency and effectiveness. Failure Mode Effect Analysis (FMEA) techniques in equipment are used to increase the reliability of preventive and predictive maintenance system. Artificial neural networks (RNA) are computational tools that find applicability in several segments of the research and signal analysis, where it is necessary to handle large amounts of data, associating statistics and computation in the optimization of dynamic processes and a high degree of reliability. They are artificial intelligence systems that have the ability to learn, are robust to failures, and can deliver real-time results. This work aims at the use of artificial neural networks to treat signals from the monitoring of tribological parameters through the use of a test bench to simulate contact failures in an air compressor in order to create an automated fault prevention system , unsupervised, with the use of self organized maps (SOM), applied to the preventive and predictive maintenance of electromechanical processes.

MEMBROS DA BANCA:
Presidente - 1753067 - EFRAIN PANTALEON MATAMOROS
Externo ao Programa - 2275732 - HERBERT RICARDO GARCIA VIANA
Externo ao Programa - 1142787 - JOSE ALFREDO FERREIRA COSTA