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Previous issue date: 2015-11-09 / O grande volume de dados gerados como resultado da supervis?o de processos de automa??o
na ind?stria gerou como consequ?ncia um vasto espa?o de armazenamento em
disco, assim como dificuldade na transmiss?o destes dados por links de telecomunica??es.
Os algoritmos de compress?o com perda de dados surgiram na d?cada de 90 com intuito
de solucionar estes problemas, passando a serem utilizados em sistemas de supervis?o industrial
para a compress?o de dados em tempo real. Para isso estes foram projetados para
eliminar informa??es redundantes e indesejadas de forma simples e eficiente. No entanto,
os par?metros destes algoritmos necessitam de serem configurados para cada vari?vel de
processo, tornando invi?vel a configura??o manual em caso de sistemas que supervisionam
milhares de vari?veis. Nesse contexto este trabalho prop?e o algoritmo Adaptive
Swinging Door Trending, que consiste numa adapta??o do Swinging Door Trending, em
que seus principais par?metros s?o ajustados dinamicamente atrav?s da an?lise de tend?ncias
do sinal. Prop?e-se tamb?m uma an?lise comparativa de desempenho dos algoritmos
de compress?o com perda de dados aplicados sobre vari?veis de processo de s?ries temporais
e cartas dinamom?tricas de fundo de po?o. Os algoritmos abordados para efeito
comparativos foram os lineares por partes e os de transformadas. / The great amount of data generated as the result of the automation and process supervision
in industry implies in two problems: a big demand of storage in discs and the
difficulty in streaming this data through a telecommunications link. The lossy data compression
algorithms were born in the 90?s with the goal of solving these problems and, by
consequence, industries started to use those algorithms in industrial supervision systems
to compress data in real time. These algorithms were projected to eliminate redundant
and undesired information in a efficient and simple way. However, those algorithms parameters
must be set for each process variable, becoming impracticable to configure this
parameters for each variable in case of systems that monitor thousands of them. In that
context, this paper propose the algorithm Adaptive Swinging Door Trending that consists
in a adaptation of the Swinging Door Trending, as this main parameters are adjusted
dynamically by the analysis of the signal tendencies in real time. It?s also proposed a
comparative analysis of performance in lossy data compression algorithms applied on
time series process variables and dynamometer cards. The algorithms used to compare
were the piecewise linear and the transforms.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/20598 |
| Date | 09 November 2015 |
| Creators | Medeiros Neto, Edson Jackson de |
| Contributors | 21929564287, http://lattes.cnpq.br/7987212907837941, Silva, Ivanovitch Medeiros Dantas da, 04665499462, http://lattes.cnpq.br/3608440944832201, Lima, F?bio Soares de, 03063679470, http://lattes.cnpq.br/9269010570877166, Oliveira, Luiz Affonso Henderson Guedes de |
| Publisher | Universidade Federal do Rio Grande do Norte, PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA EL?TRICA E DE COMPUTA??O, UFRN, Brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| 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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