doctoralThesis
Classificação automatizada de falhas tribológicas de sistemas alternativos com o uso de redes neurais artificiais não supervisionadas
Fecha
2017-01-17Registro en:
CABRAL, Marco Antonio Leandro. Classificação automatizada de falhas tribológicas de sistemas alternativos com o uso de redes neurais artificiais não supervisionadas. 2017. 140f. Tese (Doutorado em Engenharia Mecânica) - Centro de Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2017.
Autor
Cabral, Marco Antonio Leandro
Resumen
Preventing, anticipating, 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 them we can mention vibration
analysis, acoustic pressure measurement, temperature monitoring, particle analysis of
lubricating oil etc. However, electromechanical systems are complex and may exhibit
unexpected behavior. Reliability-centric 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 (ANNs) 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 realtime
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 detection and classification
system, unsupervised, with the use of self-organized maps, or SOM, applied to the preventive
and predictive maintenance of electromechanical processes.