Deep Learning-Based Prognostics for Turbofan Engine Remaining Life Prediction
DOI:
https://doi.org/10.38032/scse.2025.3.141Keywords:
AI, BiLSTM, RUL, C-MAPSS, CNN, NormalizingAbstract
In modern times, especially after the Industrial Revolution 4.0, artificial intelligence has become one of modern technology's most discussed and fascinating aspects. Artificial intelligence is now being incorporated into almost all aspects of our lives. Safety is an important field in which AI is now being implemented. Its decision-making capability from the numerical data makes it very reliable. In recent days, AI has been implemented in the predictive maintenance of various complex machines as well. This study aims to investigate the life prediction of turbofan engines. Deep learning and machine learning analyse the estimation of Remaining Useful Life (RUL). These have not only saved resources and time but also ensured greater safety to mankind. A reliable deep-learning model has been developed that can predict the RUL of a turbofan jet engine with very good reliability. The C-MAPSS dataset, which was published by NASA, was utilized to train the model. The deep learning model used here is the BiLSTM because it performs very well with sequential data and propagates both backward and forward directions. The dataset given by NASA has already been split into training and testing sets. After normalizing the data, the model has been trained with three BiLSTM layers, two fully connected layers, and one output layer. The result is quite satisfactory, with an R2 value of 0.893728 and an RSME of 13.5468187.
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Copyright (c) 2025 Md. Omar Faruk, Israt Jahan Keya, Md. Ashraful Islam (Author)
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