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Classificação de falhas em processo industrial de mineração a partir de uma representação fuzzy de séries temporais

2022; Linguagem: Inglês

10.20906/cba2022/3252

2525-8311

Gabriel Vinicios M. Fernandes, Agnaldo Rocha Reis, Frederico Gadelha Guimarães,

Fault Detection and Control Systems

Brazil is one of the most important producers of iron ore in the world and with the potential to grow over the years. The mining sector demands the adoption of new technologies for the development of specialized systems that increase the efficiency in the production process. Mining must embrace innovation for automation of repetitive tasks, systems integration, continuous process improvement, disaster risk reduction and adaptation to the global context. In this sense, the article proposes the use of Fuzzy Time Series(FTS) to learn a representation of the data that is more effective for the classification stage, using data originating from Plant A of the Ferro S11D Project, from Vale S.A., located in Canaã dos Carajás, Pará, Northern region of Brazil. The result demonstrated a significant improvement in failure prediction from the addition of PWFTS (Probabilistic Weighted Fuzzy Time Series) techniques to the XGBoost (Extreme Gradient Boosting) algorithm. From the proposed methodology, there was an increase in accuracy from 79,3% to 98,9%.