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  • A Machine Learning Approach to Forecasting Turbofan Engine Health Using Real Flight Data
    Publication . Silva, Fernanda Cavalcante da; Grinet, M.A.V.M.; Silva, André
    The modern gas turbine engine widely used for aircraft propulsion is a complex integrated system that undergoes deterioration during operation due to the degradation of its gas path components. Turbofan engine-related costs can account for as much as a third of the total aircraft maintenance costs, which have driven the industry to adopt on-condition monitoring. In this work an integrated condition monitoring platform is proposed and developed for performance analysis and prediction. Different machine learning approaches are compared with the application of predicting engine behavior aiming at finding the optimal time for engine removal. The selected models were OLS, ARIMA, NeuralProphet, and Cond-LSTM. Long operating and maintenance history of two mature CF6 turbofan engines were used for the analysis, which allowed for the identification of the impact of different factors on engine performance. These factors were also considered when training the ML models, which resulted in models capable of performing prediction under specified operation and flight conditions. The ML models provided forecasting of the EGT parameter at the take-off phase allowing for predicting gradual performance deterioration under specified operation type. Cond-LSTM is shown to be a reliable tool for forecasting engine EGT with a MAE under 5℃. In addition, forecasting engine performance parameters has shown to be useful for identifying the optimal time for performing important maintenance action, such as engine gas path cleaning. This work has shown that engine removal forecast can be more precise by using sophisticated trend monitoring and advanced ML methods.
  • False-negative Reduction in Mammography Breast Cancer Diagnosis Through Radiomics and Deep Learning
    Publication . Grinet, Marco António Vieira Macedo; Gomes, Abel João Padrão; Gouveia, Ana Isabel Rodrigues
    Traditional breast cancer diagnostic methods are heavily reliant on different medical imaging modalities. These imaging modalities, such as MG, MRI, US, and DBT, are used in breast cancer screening, treatment planning, as well as tracking disease progression. However, the process of evaluating each diagnostic image and extract relevant information from it requires a trained and experienced professional. This can be very time consuming for the medical professional, and thwarts efforts of expanding breast cancer screening to areas with a deficit of medical staff, such as rural areas away from major metropolitan centers. With the rise of digital imaging methods, DICOM, and PACS systems, it has become possible to connect patients with medical staff that reside in a different location. […]