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The paper presents a Domain Adaptive CNN-LSTM (DACL) model for predicting the Remaining Useful Life (RUL) of systems using multi-dimensional sensor data, addressing challenges posed by varying operating conditions. The DACL model integrates convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) networks for time series analysis, employing a domain adaptation technique to minimize distribution discrepancies between training and test data. Evaluated on the C-MAPSS datase

Author

Nima Rezazadeh

Language

EN