Remaining Useful Life Prediction of Turbofan Engines with Fuzzy Systems

Since the beginning of the 21st century, predictive maintenance (PdM) has gained increasing prominence in the industry, providing the capability to predict the Remaining Useful Life (RUL) of equipment and mitigate accidents and financial losses. In this context, Machine Learning (ML) models are widely employed. This study proposes the prediction of Turbofan engine RUL through a machine learning model trained with historical data from this equipment. Two distinct models were evaluated: the Fuzzy System and the Neuro-Fuzzy System. To enable training, it was necessary to select the best features using a genetic algorithm, aiming to reduce complexity and enhance the performance of the Fuzzy System. The model with the best performance was the Fuzzy System with 5 selected features and 11 Fuzzy sets. Despite not yielding the lowest RMSE metric compared to related works, this interpretable model demonstrated reasonable performance in comparison to the model by Babu et al. [8]. This suggests that, for the dataset in question, the Fuzzy system is recommended to ensure better interpretability, while neural networks used in previous studies are better suited for precise predictions.