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Previous issue date: 2017-01-17 / Prevenir, antever, evitar falhas em sistemas eletromec?nicos s?o demandas que desafiam
pesquisadores e profissionais de engenharia a d?cadas. Sistemas eletromec?nicos apresentam
processos tribol?gicos que resultam em fadiga de materiais e consequente perda de efici?ncia
ou mesmo de utilidade de m?quinas e equipamentos. Diversas t?cnicas s?o utilizadas na
tentativa de, atrav?s da an?lise de sinais oriundos dos equipamentos estudados, que seja
poss?vel a minimiza??o das perdas inerentes ?queles sistemas e as consequ?ncias desses
desgastes em momentos n?o esperados, como uma aeronave em voo ou uma perfuratriz em
um po?o de petr?leo. Dentre elas podemos citar a an?lise de vibra??o, medi??o da press?o
ac?stica, monitoramento de temperatura, an?lise de part?culas de ?leo lubrificante etc.
Entretanto sistemas eletromec?nicos s?o complexos e podem apresentar comportamentos
inesperados. A manuten??o centrada na confiabilidade necessita de recursos tecnol?gicos
cada vez mais r?pidos, eficientes e robustos para garantir sua efici?ncia e efic?cia. T?cnicas
de an?lise de efeitos e modos de falha (FMEA ? Failure Mode Effect Analysis) em
equipamentos s?o utilizadas para aumentar a confiabilidade dos sistemas de manuten??o
preventiva e preditiva. As redes neurais artificiais (RNA) s?o ferramentas computacionais que
encontram aplicabilidade em diversos segmentos da pesquisa e an?lise de sinais, onde h?
necessidade do manuseio de grandes quantidades de dados, associando estat?stica e
computa??o na otimiza??o de processos din?micos e um alto grau de confiabilidade. S?o
sistemas de intelig?ncia artificial que t?m capacidade de aprender, s?o robustas a falhas e
podem apresentar resultados em tempo real. Este trabalho tem como objetivo a utiliza??o de
redes neurais artificiais para tratar sinais provenientes da monitora??o de par?metros
tribol?gicos atrav?s do uso de uma bancada de testes para simular falhas de contato em um
compressor de ar, a fim de criar um sistema de detec??o e classifica??o de falhas
automatizado, n?o supervisionado, com o uso de mapas auto-organiz?veis, ou redes SOM (self
organizaed maps), aplicado ? manuten??o preventiva e preditiva de processos
eletromec?nicos. / 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.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/22518 |
| Date | 17 January 2017 |
| Creators | Cabral, Marco Antonio Leandro |
| Contributors | 21896458831, http://lattes.cnpq.br/5336356193599447, Viana, Herbert Ricardo Garcia, 83935720459, http://lattes.cnpq.br/4617469809005234, Silva, Jo?o Bosco da, 13163191487, http://lattes.cnpq.br/3305848313356239, Paiva, Jos? Alvaro de, 79173497487, http://lattes.cnpq.br/6136888701626547, Morais, Antonio Higor Freire de, 04644618470, http://lattes.cnpq.br/7568055799308361, Oliveira, Adelci Menezes de, 67187366434, http://lattes.cnpq.br/7673440916658787, Costa, Jos? Alfredo Ferreira, Matamoros, Efrain Pantaleon |
| Publisher | PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA MEC?NICA, UFRN, Brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | Portuguese |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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