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Authors
Abstract(s)
Nos tempos actuais os equipamentos para Aquecimento Ventilação e Ar Condicionado (AVAC) ocupam um lugar de grande importância na concepção, desenvolvimento e manutenção de qualquer edifício por mais pequeno que este seja.
Assim, surge a necessidade premente de racionalizar os consumos energéticos optimizando-os. A alta fiabilidade desejada nestes sistemas obriga-nos cada vez mais a descobrir formas de tornar a sua manutenção mais eficiente, pelo que é necessário prevenir de uma forma proactiva todas as falhas que possam prejudicar o bom desempenho destas instalações. Como tal, torna-se necessário detectar estas falhas/anomalias, sendo imprescíndivel que nos antecipemos a estes eventos prevendo o seu acontecimento num horizonte temporal pré-definido, permitindo actuar o mais cedo possível.
É neste domínio que a presente dissertação tenta encontrar soluções para que a manutenção destes equipamentos aconteça de uma forma proactiva e o mais eficazmente possível. A ideia estruturante é a de tentar intervir ainda numa fase incipiente do problema, alterando o comportamento dos equipamentos monitorizados, de uma forma automática, com recursos a agentes inteligentes de diagnóstico de falhas.
No caso em estudo tenta-se adaptar de forma automática o funcionamento de uma
Unidade de Tratamento de Ar (UTA) aos desvios/anomalias detectadas, promovendo a paragem integral do sistema apenas como último recurso.
A arquitectura aplicada baseia-se na utilização de técnicas de inteligência artificial, nomeadamente dos sistemas multiagente. O algoritmo utilizado e testado foi construído em Labview®, utilizando um kit de ferramentas de controlo inteligente para Labview®.
O sistema proposto é validado através de um simulador com o qual se conseguem reproduzir as condições reais de funcionamento de uma UTA.
In the current days the HVAC (Heating, Ventilation and Ar Conditioning) equipment have a great influence in the conception, development and maintenance of any building no matter how small it is. Thus, it is necessary some system optimization due to a high necessity to rationalize costs. The reliability needed in these systems turn necessary to find more ways to make the maintenance more efficient, and proactively detect the faults that could brake the good performance of these installations. Therefore, it is a real need to detect as soon as possible these faults, forecasting it’s occurrence in a predefined horizon, with knowledge and cause knowing. It is in this dominium, that this thesis tries to find solutions for the maintenance of these equipments, in a proactive way. The structuring idea is trying to act as soon as possible, changing the monitored equipment behaviour, trying to reduce non-expected deviations, by actuating directly on the system, using fault tolerant control intelligent agents. In the case study it is tried to accommodate the functioning of a Air Handling Unit (AHU) to the detected faults, promoting the integral shutdown only as last resource. The used architecture is based on artificial intelligence techniques, namely multiagent systems. The used and tested algorithm was built in Labview®, using the Intelligent Control ToolKit for Labview® (ICTL). The proposed system is validated in a simulator, where we can reproduce an AHU in real operating conditions.
In the current days the HVAC (Heating, Ventilation and Ar Conditioning) equipment have a great influence in the conception, development and maintenance of any building no matter how small it is. Thus, it is necessary some system optimization due to a high necessity to rationalize costs. The reliability needed in these systems turn necessary to find more ways to make the maintenance more efficient, and proactively detect the faults that could brake the good performance of these installations. Therefore, it is a real need to detect as soon as possible these faults, forecasting it’s occurrence in a predefined horizon, with knowledge and cause knowing. It is in this dominium, that this thesis tries to find solutions for the maintenance of these equipments, in a proactive way. The structuring idea is trying to act as soon as possible, changing the monitored equipment behaviour, trying to reduce non-expected deviations, by actuating directly on the system, using fault tolerant control intelligent agents. In the case study it is tried to accommodate the functioning of a Air Handling Unit (AHU) to the detected faults, promoting the integral shutdown only as last resource. The used architecture is based on artificial intelligence techniques, namely multiagent systems. The used and tested algorithm was built in Labview®, using the Intelligent Control ToolKit for Labview® (ICTL). The proposed system is validated in a simulator, where we can reproduce an AHU in real operating conditions.
Description
Keywords
Manutenção proactiva Sistemas multiagente Unidade de tratamento ar Lógica fuzzy Proactive maintenance Multiagent systems Air handling unit Fuzzy logic
Citation
Silva, Pedro Luis Gonçalves da - Manutenção proactiva de sistemas AVAC com recurso aos sistemas inteligentes multiagente. Lisboa: Instituto Superior de Engenharia de Lisboa, 2011. Dissertação de mestrado.