Implementação da manutenção preditiva: Uma abordagem proativa na manutenção 4.0

Abstract

The research objective of this dissertation addresses the implementation of predictive maintenance in the Brazilian Navy (MB), highlighting the theoretical foundations, historical evolution, and advantages of this approach compared to traditional corrective and preventive practices. Initially, the basic concepts and history of predictive maintenance are discussed, which uses technologies such as machine learning, big data, and artificial neural networks to monitor and predict equipment failures before they occur. The dissertation also analyzes the current maintenance situation in the MB, identifying problems such as high dependency on corrective methods, lack of resources and qualified personnel, and an aging fleet. The need for modernization is highlighted, proposing a transition to predictive practices that increase operational efficiency and reduce costs. Case studies, including those from the Portuguese Navy and the United States Navy, are presented to illustrate the benefits and challenges of predictive maintenance, showing significant reductions in operational costs and improvements in equipment availability. The dissertation concludes with practical recommendations for implementing predictive maintenance in the MB, emphasizing the importance of strategic commitment, investment in advanced technologies, and continuous personnel training, suggesting that this approach is essential to meet contemporary and future challenges, ensuring greater operational readiness and safety in maritime operations.