Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-08-16T20:26:43Z No. of bitstreams: 1 IsacBarbosaDeAlmeida_DISSERT.pdf: 2821753 bytes, checksum: df59d2e78eb7f99cfa9a96085f3eae4c (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-08-18T21:53:45Z (GMT) No. of bitstreams: 1 IsacBarbosaDeAlmeida_DISSERT.pdf: 2821753 bytes, checksum: df59d2e78eb7f99cfa9a96085f3eae4c (MD5) / Made available in DSpace on 2016-08-18T21:53:45Z (GMT). No. of bitstreams: 1 IsacBarbosaDeAlmeida_DISSERT.pdf: 2821753 bytes, checksum: df59d2e78eb7f99cfa9a96085f3eae4c (MD5) Previous issue date: 2016-05-06 / Com a intensa utiliza??o de rolamentos em diversos segmentos da ind?stria, h? um elevado n?mero de pausas necess?rias nos processos industriais para a realiza??o de manuten??es nesses dispositivos, tendo como estudo de caso os aerogeradores. O crescimento do setor de energia e?lica, incentivou a realizar uma pesquisa que auxilie a solu??o desse problema. Para contribuir com a manuten??o preventiva foi realizado uma an?lise de sinais aplicando t?cnicas que permitem a detec??o e a localiza??o do problema a fim de evitar acidentes e preju?zos ocasionados por falhas inesperadas nos equipamentos, visto que a baixa rota??o do sistema dificulta a detec??o da falha. Para solucionar esse item, realizou-se a determina??o de sinais padr?o para os defeitos nos rolamentos, facilitando o diagn?stico de poss?veis falhas. Com esse diagn?stico pode ser executada uma manuten??o preventiva, identificando a falha do sistema que foram testadas, como a introdu??o de gr?os de areia no rolamento, desgaste na pista externa do rolamento e oxida??o do rolamento. Atrav?s do processamento de sinais ? poss?vel construir os gr?ficos desenvolvendo um mapeamento dos defeitos atrav?s de diferentes picos nas faias de frequ?ncia. / With the heavy use of bearings in various segments of the industry, there are a large number of necessary interruptions in industrial processes to perform maintenance on these devices, with the case study wind turbines. The growth of the wind energy sector, encouraged to conduct research that helps to solve this problem. To contribute to predictive maintenance has been carried out a signal analysis using techniques which allow detection and location of the problem in order to prevent accidents caused and losses due to unexpected equipment failures, whereas low system rotation complicates the detection of the failure. To work around this problem, there was the indication of standard signals for defects in the bearings, making diagnosis of possible failures. With this diagnosis can be performed predictive maintenance, identifying the failure of the system that were tested, such as the introduction of grains of sand in the bearing, wear on the outer race of the bearing and bearing rust. By processing signals it is possible to construct graphs developing a mapping of defects by different peaks in the frequency band.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-05-30T23:20:43Z No. of bitstreams: 1 AlineCristinaMendesDeFarias_TESE.pdf: 9556716 bytes, checksum: 2350df4b9bbb084a66c9da511159a31d (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-06-01T23:15:21Z (GMT) No. of bitstreams: 1 AlineCristinaMendesDeFarias_TESE.pdf: 9556716 bytes, checksum: 2350df4b9bbb084a66c9da511159a31d (MD5) / Made available in DSpace on 2016-06-01T23:15:21Z (GMT). No. of bitstreams: 1 AlineCristinaMendesDeFarias_TESE.pdf: 9556716 bytes, checksum: 2350df4b9bbb084a66c9da511159a31d (MD5) Previous issue date: 2015-07-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / A lubricidade do biodiesel interfere no funcionamento normal dos sistemas de inje??o diesel, principalmente dos bicos injetores, reduzindo o desgaste e elevando o tempo de vida da linha de alimenta??o do motor. O m?todo HFRR (High Frequency Reciprocating Rig) ? um ensaio padr?o para avalia??o da lubricidade do diesel/biodiesel (ASTM D6079), consistindo principalmente da medi??o da escara de desgaste impressa na esfera numa sonda de movimento alternado sob alta frequ?ncia. Quando o desgaste se desenvolve, algumas caracter?sticas din?micas do sistema variam, resultando em desvios significativos nos padr?es dos sinais de vibra??o, ac?sticos e de atrito. A aquisi??o e caracteriza??o das assinaturas desses sinais obtidos durante os ensaios tribol?gicos representam uma importante ferramenta n?o intrusiva de avalia??o da lubricidade desses combust?veis, cuja representa??o no dom?nio do tempo nem sempre revelam informa??es triviais, sendo necess?ria sua transforma??o para o dom?nio da frequ?ncia usando a Transformada r?pida de Fourier (FFT) do sinal. A presente tese objetivou desenvolver uma forma din?mica de avalia??o de lubricidade de combust?veis (Diesel S50, ?ster Met?lico de Soja e blendas B10 e B20) em bancada HFRR (contato esfera-disco plano), e melhoria aos referenciais metodol?gicos da ASTM D6079, aplicando t?cnicas de an?lise de sinais (vibra??o e n?vel de press?o sonora) e sua associa??o com par?metros disponibilizados pela norma ASTM D6079 (di?metro da escara de desgaste) com dura??o de 75, 120 e 180 minutos. Os perfis e par?metros de rugosidade e an?lise microsc?pica dos discos foram usados para melhor avaliar a influ?ncia do tempo e do combust?vel na evolu??o do desgaste do disco. Nesta avalia??o, a lubricidade dos combust?veis aumentou com o teor de biodiesel utilizado e diminuiu com o aumento do n?mero de ciclos aplicado. A an?lise temporal e o espectro de frequ?ncia dos sinais demonstraram sensibilidade com a mudan?a do combust?vel, o n?mero de ciclos e, consequentemente, com a evolu??o do desgaste pelo uso prolongado dos combust?veis, estando, assim, correlacionados entre si e com os resultados dispon?veis pela ASTM. / The lubricity of biodiesel interferes in the normal operation of diesel injection systems, especially in nozzles. It can reduce the wear and raise the lifetime of the engine supply line. The method HFRR (High Frequency Reciprocating Rig) is a standard test for evaluation of diesel/ biodiesel lubricity (ASTM D6079). The HFRR lubricity evaluation mainly consists of measuring the printed scar wear on the ball. When wear is developing, some dynamic features of the system vary and it results in significant changes in the patterns of vibration, sound pressure level (SPL) and friction signals. The acquisition and characterization of signatures of these signals obtained during tribological tests are an important non-intrusive tool for evaluating of the lubricity of these fuels, whose representation in the time domain not always reveals trivial information. This requires signal transformation to the frequency domain using the Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) analyses. This thesis aimed to develop a dynamic assessment of lubricity fuels (Diesel S50, Methyl Ester of Soybean and B10 and B20 blends) in HFRR bench (ball-on-flat disk contact), and improving the methodological framework of ASTM D6079 standard by application of signal analysis techniques (vibration and SPL) and its association with parameters provided by ASTM D6079 (wear scar diameter) lasting 75, 120 and 180 minutes. The profiles and topographic parameters of scars and their microscopic analysis were used to further evaluate the influence of the time and fuel in the wear evolution of disk. In this evaluation, the fuel lubricity was influenced by increasing of biodiesel content and decreasing of number of cycles applied. From the temporal and frequency spectrum analysis of the vibration and NPS signals it is shown that both vibration and NPS stablish a relation with fuel lubricity, biodiesel content, number of cycles and parameters of profiles from scar of discs.

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-03-28T19:32:54Z No. of bitstreams: 1 MarcoAntonioLeandroCabral_TESE.pdf: 13589109 bytes, checksum: dcecde654045d4bb434b8363031ec773 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-03-29T19:12:55Z (GMT) No. of bitstreams: 1 MarcoAntonioLeandroCabral_TESE.pdf: 13589109 bytes, checksum: dcecde654045d4bb434b8363031ec773 (MD5) / Made available in DSpace on 2017-03-29T19:12:55Z (GMT). No. of bitstreams: 1 MarcoAntonioLeandroCabral_TESE.pdf: 13589109 bytes, checksum: dcecde654045d4bb434b8363031ec773 (MD5) 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.