Predictive maintenance mechanisms for heating equipment

• Autor(a) principal: Santiago, Ana Rita Antunes
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/29723
• topic: Big Data applications; Machine Learning; HVAC; Predictive Maintenance; Data processing; Data Analysis

Heating appliances such as HVAC systems are susceptible to failures that may result in disruption of important operations. With this in mind, it is relevant to increase the efficiency of those solutions and diminish the number of detected faults. Moreover, understand why these failures occur that be relevant for future devices. Thus, there is a need to develop methods that allow the identification of eventual failures before they occur. This is only achievable when solutions capable of analyzing data, interpret it and obtaining knowledge from it, are created. This dissertation presents an infrastructure that supports the inspection of failure detection in boilers, making viable to forecast faults and errors. A major part of the work is data analysis and the creation of procedures that can process it. The main goal is creating an efficient system able to identify, predict and notify the occurrence of failure events.