Percorrer por autor "GUILHERME, DAVID NUNO VILAS BOAS"
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- Remaining useful life prediction on the NASA CMAPSS dataset comparing LSTM and transformer modelsPublication . GUILHERME, DAVID NUNO VILAS BOAS; Ramos, Carlos Fernando da SilvaPredictive maintenance has been gaining importance in industry, especially in complex and critical systems, such as turbofan engines used in aviation. The main objective on this dissertation is the prediction of the Remaining Useful Life (RUL) of jet engines, using the dataset provided by NASA, known as Commercial Modular Aero-Propulsion System Simulation (CMAPSS). Accurate RUL estimation reduces maintenance costs, prevents unexpected failures and improves operational safety. This research began with a detailed dataset analysis, exploring its different subsets, each representing distinct operating conditions and fault modes. Data preprocessing was then performed, including normalization, feature selection, and construction of temporal sequences. Feature selection techniques were also applied, such as low variance and high correlation filters as well as Boruta method, to reduce the number of features used. Thus, only selecting variables with real impact on RUL were employed in model training. Subsequently, two models were implemented based on architectures studied in the literature. The first model, based on Long Short-Term Memory (LSTM) networks, leverages their ability to capture long term temporal dependencies. The second model was a Transformer, whose main innovation lies in the attention mechanism. Experimental results were evaluated using the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. The LSTM model achieved competitive performance on FD001, confirming prior studies that highlight it as a robust baseline for simple scenarios. The Transformer showed an advantage on the FD002 subset. However, in more complex subsets such as FD004, the performance of both models converged, reflecting the remaining challenges in generalizing these models. The comparison between LSTM and Transformer revealed that LSTMs are more consistent in controlled scenarios with simple, well defined operating conditions. The transformer demonstrated potential in datasets with greater variability, such as FD002, although its results were not consistent across all subsets. These contrasts reinforce the idea that, in their current state, LSTMs remain a dependable choice, while Transformers still face generalization challenges. Nevertheless, the literature points to future improvements, particularly through the implementation of hybrid architectures or specialized variants, which may overcome these limitations. In summary, this dissertation contributes to the advancement of knowledge in predictive maintenance by providing a comparative analysis between two of the most relevant architectures. The results reinforce the need to continue exploring innovative model and methodology combinations to develop prognostic systems that are increasingly accurate, interpretable and applicable in real industrial scenarios.