Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-06-03T23:48:41Z No. of bitstreams: 1 EdsonJacksonDeMedeirosNeto_DISSERT.pdf: 2349422 bytes, checksum: 58dbff032ce7a77c69ab57cce418be2d (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-06-07T19:55:39Z (GMT) No. of bitstreams: 1 EdsonJacksonDeMedeirosNeto_DISSERT.pdf: 2349422 bytes, checksum: 58dbff032ce7a77c69ab57cce418be2d (MD5) / Made available in DSpace on 2016-06-07T19:55:39Z (GMT). No. of bitstreams: 1 EdsonJacksonDeMedeirosNeto_DISSERT.pdf: 2349422 bytes, checksum: 58dbff032ce7a77c69ab57cce418be2d (MD5) 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.