Cabral, Marco Antonio LeandroCavalcanti, André Fonseca2025-04-092025-04-092025-02-07CAVALCANTI, André Fonseca. Machinery Guardian: otimizando manutenção com inteligência artificial e autonomia. Orientador: Dr. Marco Antonio Leandro Cabral. 2025. 54f. Dissertação (Mestrado Profissional em Ciência, Tecnologia e Inovação) - Escola de Ciências e Tecnologia, Universidade Federal do Rio Grande do Norte, Natal, 2025.https://repositorio.ufrn.br/handle/123456789/63434The advancement of Industry 4.0, characterized by the integration of intelligent systems into production processes, presents challenges and opportunities for asset maintenance, focusing on the adoption of autonomous technologies and artificial intelligence. This study investigates the implementation of autonomous maintenance systems, analyzing how these technologies can optimize the detection and classification of failures in cyberphysical systems, enhancing equipment availability and reliability. The research was conducted using real-time data analysis methodologies, based on sensors and AI algorithms, resulting in precise diagnostics and more effective interventions. The results indicate that the incorporation of artificial intelligence into maintenance systems not only increases operational efficiency but also reduces costs and minimizes risks, pointing to a future where autonomous maintenance becomes a standard in advanced industry.Acesso AbertoIndústria 4.0Manutenção autônomaSistemas ciberfísicosOtimização de processoInteligência artificialmasterThesisCNPQ::OUTROS::CIENCIAS