A New Hybrid Model for RUL Prediction through Machine Learning

Remaining useful life (RUL) prediction plays a significant role in prognostics and health management systems. While three different approaches have been utilized to estimate the RUL, hybrid-based methodologies yield more accurate results in this field. This study aims to introduce a hybrid prognostic approach based on deep learning methods, including long short-term memory (LSTM) and convolutional neural network (CNN). In most of the combined models, CNN is using to extract the features, and then, these LSTM be fed by extracted information from CNN, but in the hybrid model, both LSTM and CNN use organically to enhance the prediction ability. Besides, the time window (TW) is utilized to provide sequential data by sliding it on input data. To evaluate the proposed model's accuracy and speed, the KPCA algorithm is used to determine the dependency of the model on extracted features. The proposed model is validated on the data developed by NASA's commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The results have illustrated that removing less important features has no effect on the proposed model.