<p>The overall focus of this thesis is the implementation of a process monitoring system in a real manufacturing environment that utilizes multivariate analysis techniques to assess the state of the process. The process in question was the medium-high volume manufacturing of discrete aluminum parts using relatively simple machining processes involving the use of two tools. This work can be broken down into three main sections.</p><p>The first section involved the modeling of temperatures and thermal expansion measurements for real-time thermal error compensation. Thermal expansion of the Z-axis was measured indirectly through measurement of the two quality parameters related to this axis with a custom gage that was designed for this part. A compensation strategy is proposed which is able to hold the variation of the parts to ±0.02mm, where the tolerance is ±0.05mm.</p><p>The second section involved the modeling of the process data from the parts that included vibration, current, and temperature signals from the machine. The modeling of the process data using Principal Component Analysis (PCA), while unsuccessful in detecting minor simulated process faults, was successful in detecting a miss-loaded part during regular production. Simple control charts using Hotelling's T^2 statistic and Squared Prediction Error are illustrated. The modeling of quality data from the process data of good parts using Projection to Latent Structures by Partial Least Squares (PLS) data did not provide very accurate fits to the data; however, all of the predictions are within the tolerance specifications.</p><p>The final section discusses the implementation of a process monitoring system in both manual and automatic production environments. A method for the integration and storage of process data with Mitutoyo software MCOSMOS and MeasurLink® is described. All of the codes to perform multivariate analysis and process monitoring were written using Matlab.</p> / Thesis / Master of Applied Science (MASc)

第22回名古屋大学年代測定総合研究センターシンポジウム平成21(2009)年度報告

Made available in DSpace on 2014-08-13T14:50:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-07-15Bitstream added on 2014-08-13T18:01:19Z : No. of bitstreams: 1 santos_gl_me_ilha.pdf: 4835852 bytes, checksum: 8f5c7964f7834bad1240378b2d45737b (MD5) / As técnicas de termografia vêm atualmente ganhando espaço como técnicas de manutenção preditiva, principalmente, por seu caráter não invasivo (ferramenta de não contato) que possibilita o monitoramento do aquecimento de máquinas e equipamentos em operação ou mesmo energizados. A utilização de câmeras termográficas hoje é uma realidade em vários setores industriais para monitoramento e detecção de falhas com base na temperatura. Entretanto, a utilização de câmaras termográficas na manutenção não deve se restringir apenas à avaliação da temperatura, uma vez que as imagens termográficas são sinais que apresentam padrões complexos que podem captar as diferentes características e condição de operação do equipamento. Outras informações além da temperatura poderiam ser observadas para uma avaliação mais consistente do estado de operação do equipamento. Este trabalho discute a utilização da técnica da estatística multivariada, Análise de Componentes Principais (ACP) para o processamento e análise de um conjunto de imagens termográficas. Essa proposta visa à identificação de padrões associados às variações térmicas das imagens, bem como, a interpretação desses dados em termos da sua variabilidade espacial/temporal para aplicação na manutenção preditiva com base na termografia. Num primeiro momento a técnica foi aplicada para a avaliação de um conjunto de dados (imagens térmicas) obtidos a partir da simulação do aquecimento de um dado componente (chave elétrica), cujo objetivo foi testar e verificar a validade da proposta e do programa desenvolvido. Posteriormente a técnica foi aplicada para o acompanhamento e avaliação do aquecimento de componentes de um modelo simplificado de um painel de telefonia, formado por blocos de alumínio fixados em uma placa de acrílico. A análise no modo espacial e no modo temporal do conjunto de ... (Resumo completo, clicar acesso eletrônico abaixo) / Thermography techniques are currently gaining ground as predictive maintenance techniques, mainly due to its non-invasive character (non-contact tool) that allows the monitoring of heating condition of machines and equipment also in operation and even energized. The use of thermographic cameras is now a reality in many industrial and electrical sectors for monitoring and fault detection based on temperature. However, the use of thermal imagers in the maintenance should not be restricted to only the evaluation of temperature, since the thermographic images are signs that show complex patterns and they can capture the different characteristics of the actual condition of the monitored equipment. Information other than temperature could be observed for a more consistent evaluation of its state of operation. This paper discusses the propose of use of the multivariate analysis technique, Principal Component Analysis (PCA) for the processing and analysis of a series of thermographic images in order to identify patterns associated with temperature variations of the images, as well as, the interpretation of these data in terms of their spatial/temporal variability. Initially the technique was used to the analysis of data (thermal images) obtained from the simulation of heating conditions of a component (electric switch) aiming at to test and verify the validity of the proposal and program development. Later the technique was applied to the monitoring and evaluation of the heating condition of components of a simplified model of a telephone panel, formed by aluminum blocks fixed in a plate of acrylic. The analysis in the spatial and temporal mode of the set of thermograms obtained for different heating conditions of the blocks, it showed that it is possible to verify and establish correlations between the Principal Components and the thermal profile of the system

Orientador: João Antonio Pereira / Banca: Amarildo Tabone Paschoalini / Banca: Renê Pegoraro / Resumo: As técnicas de termografia vêm atualmente ganhando espaço como técnicas de manutenção preditiva, principalmente, por seu caráter não invasivo (ferramenta de não contato) que possibilita o monitoramento do aquecimento de máquinas e equipamentos em operação ou mesmo energizados. A utilização de câmeras termográficas hoje é uma realidade em vários setores industriais para monitoramento e detecção de falhas com base na temperatura. Entretanto, a utilização de câmaras termográficas na manutenção não deve se restringir apenas à avaliação da temperatura, uma vez que as imagens termográficas são sinais que apresentam padrões complexos que podem captar as diferentes características e condição de operação do equipamento. Outras informações além da temperatura poderiam ser observadas para uma avaliação mais consistente do estado de operação do equipamento. Este trabalho discute a utilização da técnica da estatística multivariada, Análise de Componentes Principais (ACP) para o processamento e análise de um conjunto de imagens termográficas. Essa proposta visa à identificação de padrões associados às variações térmicas das imagens, bem como, a interpretação desses dados em termos da sua variabilidade espacial/temporal para aplicação na manutenção preditiva com base na termografia. Num primeiro momento a técnica foi aplicada para a avaliação de um conjunto de dados (imagens térmicas) obtidos a partir da simulação do aquecimento de um dado componente (chave elétrica), cujo objetivo foi testar e verificar a validade da proposta e do programa desenvolvido. Posteriormente a técnica foi aplicada para o acompanhamento e avaliação do aquecimento de componentes de um modelo simplificado de um painel de telefonia, formado por blocos de alumínio fixados em uma placa de acrílico. A análise no modo espacial e no modo temporal do conjunto de ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Thermography techniques are currently gaining ground as predictive maintenance techniques, mainly due to its non-invasive character (non-contact tool) that allows the monitoring of heating condition of machines and equipment also in operation and even energized. The use of thermographic cameras is now a reality in many industrial and electrical sectors for monitoring and fault detection based on temperature. However, the use of thermal imagers in the maintenance should not be restricted to only the evaluation of temperature, since the thermographic images are signs that show complex patterns and they can capture the different characteristics of the actual condition of the monitored equipment. Information other than temperature could be observed for a more consistent evaluation of its state of operation. This paper discusses the propose of use of the multivariate analysis technique, Principal Component Analysis (PCA) for the processing and analysis of a series of thermographic images in order to identify patterns associated with temperature variations of the images, as well as, the interpretation of these data in terms of their spatial/temporal variability. Initially the technique was used to the analysis of data (thermal images) obtained from the simulation of heating conditions of a component (electric switch) aiming at to test and verify the validity of the proposal and program development. Later the technique was applied to the monitoring and evaluation of the heating condition of components of a simplified model of a telephone panel, formed by aluminum blocks fixed in a plate of acrylic. The analysis in the spatial and temporal mode of the set of thermograms obtained for different heating conditions of the blocks, it showed that it is possible to verify and establish correlations between the Principal Components and the thermal profile of the system / Mestre

A growing recognition of the need for effective marine environmental management as a result of the increasing exploitation of marine biological resources has highlighted the need for high speed ecological seabed mapping. The practice of mapping making extensive use of satellite remote sensing and airborne platforms is well established for terrestrial management. Marine biological resource mapping however is not readily available except in part from that derived for surface waters from satellite based ocean colour mapping. Perhaps the most fundamental reason is that of sampling difficulty, which involves broad areas of seabed coverage, irregularities of seabed surface and depth. Conventional grab sample techniques are widely accepted as a standard seabed mapping methodology that has been in use long before the advent of acoustic techniques and continue to be employed. However. they are both slow and labour intensive, factors which severely limit the spatial coverage available from practical grab sampling programs. While acoustic techniques have been used for some time in pelagic biomass assessment, only recently have acoustic techniques been applied to marine biological resource mapping of benthic communities. Two commercial bottom classifiers available in the market that use normal incidence echosounders are the RoxAnn and QTC View systems. Users and practitioners should be cautious however when using black box implementations of the two commercial systems without a proper quality control over raw acoustic data since some researchers in their studies have indicated problems with these two bottom classifiers such as, among others, a depth dependence. In this thesis, an alternative approach was adopted to the use of echosounder returns for bottom classification. / The approach used in this study is similar to,~ used in the commercial RoxAnn system. In grouping bottom types however, Multivariate analysis (Principal Component Analysis and Cluster Analysis) was adopted instead of the allocation system normally used in the RoxAnn system, called RoxAnn squares. In addition, the adopted approach allowed for quality control over acoustic data before further analysis was undertaken. As a working hypothesis, it was assumed that on average 0 and aE2 = 0 where E1 and E2 are the roughness and hardness indices, respectively, and RO is the depth. For roughness index (E1), this was achieved by introducing a constant angular integration interval to the tail of the first OM returns whereas for hardness index (E2), this was achieved by introducing a constant depth integration interval. Since three different frequencies, i.e. 12, 38 and kHz, were operated, Principal Component Analysis was used here to reduce the dimensionality of roughness and hardness indices, formed from the three operated qu frequencies separately. The k-means technique was applied to the first principal component of roughness index and the first principal comp component of hardness index to produce separable seabed types. This produced four separable seabed types, namely soft-smooth, soft-rough, hard-smooth and hard-rough seabeds. / Principal Component Analysis was also used to reduce the dimensionality of the area backscattering coefficient sA, a relative measure of biomass of benthic mobile biota. The bottom classification results reported here appear to be robust in that, where independent ground truthing was available, acoustic classification was generally congruent with ground truth results. When investigating the relationship between derived bottom type and acoustically assessed total biomass of benthic mobile biota, no trend linking the two parameters, however, appears. Nevertheless, using the hierarchical agglomerative technique applied to a set of variables containing average first principal component of the area backscattering coefficient sA, the average first principal component of roughness and hardness indices, the centroids of first principal component of roughness and hardness indices associated with the four seabed types and species composition of fish group of the common species in trawl stations available, two main groups of quasi acoustic population are observed in the North West Shelf (NWS) study area and three groups are observed in the South East Fisheries (SEF) study area. The two main groups of quasi acoustic population in the NWS study area and the three main groups of quasi acoustic population in the study area are associated with the derived seabed types and fish groups of the common species.