Predicting Aircraft Turbofan Engine Degradation with Recurrent Neural Networks

Machine learning and deep learning models can be developed to forecast the possibility of a particular outcome in a phenomenon with the help of available data. The paper tackles the problem of predicting the Remaining Useful Life of Aircraft Turbofan engine. The paper's goal is to use machine learning to estimate remaining useful life, or RUL. RUL is the amount of time that a component has left to operate satisfactorily before failing. The model needs to be thoroughly validated using Prognostics metrics and provide forecast uncertainty. When using Remaining Useful Life in conjunction with Predictive Maintenance processes, a machine can be kept dependable and efficient while experiencing less downtime and operational expenses. RNNs are a type of artificial neural network (ANN) that are well-suited for sequential data, such as sensor data from aircraft engines. RNNs are able to learn from the temporal dependencies in the data, which can help to improve the accuracy of RUL predictions. Several different RNN architectures have been used for RUL prediction, including long short-term memory (LSTM) networks and gated recurrent units (GRUs). LSTM networks are particularly wellsuited for tasks that require long-term memory, while GRUs are simpler and more efficient to train. In recent years, RNNs have been combined with other machine learning techniques, such as convolutional neural networks (CNNs) and attention mechanisms, to further improve RUL prediction accuracy.