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The adoption of Industry 4.0 tools brings significant added value to industrial companies. These technologies enable the processing of massive amounts of data in real time, the identification of complex patterns, and the prediction of critical events such as failures or production deviations. Integrating neural networks into industrial processes supports faster decision-making, more effective predictive maintenance, and continuous performance improvement, thus contributing to smarter, more responsive, and more competitive production. In this research, we will study a case of predictive maintenance on a production line; the goal of study is to develop an intelligent maintenance system capable to predict downbreaks relied to the historic data based on neural network technology.
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DOI: 10.1109/iccsc66714.2025.11134932
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