Desenvolvimento e aplicação de módulo de sensoriamento de baixo custo para o monitoramento de saúde estrutural de máquina rotativa

• Autor(a) principal: Mascoloti, Gabriela Tavares
• Data de Publicação: 2021
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/15215
• topic: Manutenção preditiva; IoT; Manutenção; MEMS; Predictive; Maintenance; ENGENHARIAS::ENGENHARIA MECANICA::MECANICA DOS SOLIDOS

Predictive maintenance is a recent maintenance model on the market, although it is already well known in the aeronautical industry, and it is increasingly necessary in any manufacturing environment. The research project aims to develop low-cost reduction nodes for predictive monitoring of machine health, increasing production performance and reducing long-term maintenance costs. Integrated with the concept of Internet of Things (IoT) and data processing, it will result in a tool to increase machine reliability. Precisely because it is a low-cost development, which will be developed in a laboratory with hardware and commercial sensor, the main challenges of the project is the dimensioning in such a way that it meets the requirements of an industrial environment and is economically advantageous. In addition, once developed, the technical staff responsible for monitoring must be trained to correctly read the information that will be treated and provided from a communication platform with the cloud. Another challenge of the research is to be able to install the prototype developed in a factory and prove the efficiency of the method. The focus of the main research is to develop a predictive monitoring device that is easy to install and operate with low cost, integrated with data treatment via the cloud and possible Application for industry. Once this goal is achieved, the device will be tested in a laboratory and industrial environment for study validation and data comparison. The study will focus on comparing the performance of machines with and without monitoring via a device developed in the research, in order to prove the importance and necessity of predictive maintenance aligned with data processing in the industry.