A Hybrid Prognostic Approach Based on Deep Learning for the Degradation Prediction of Machinery

Remaining useful life (RUL) prediction is of great significance for prognostic and health management (PHM) as it can achieve more reliable and effective maintenance strategies. With the advances in the field of deep learning, data-driven methods have provided promising prognostic prediction results. Hence, this research presents a data-driven prognostic approach based on deep learning models for predicting the RUL of mechanical systems effectively. Multiple separable convolution layers, a bidirectional Long Short-Term Memory (LSTM) layer, and fully-connected layers (FCL) are included in the proposed network, named the SC-BLSTM, to accomplish more accurate prognostic prediction from the raw degradation data acquired by different sensors. The proposed SC-BLSTM approach aims to learn complex and nonlinear features from the input data and capture temporal dependencies from the learned features. The presented approach in this research is tested and verified on the degradation data of turbofan engines (C-MAPSS dataset) from NASA. The result demonstrated that the SC-BLSTM is able to achieve more effective RUL prediction compared with some existing prognostic models. value. This shows that the performance of the RUL prediction improves when the testing turbofan engines are close to failure. The prognostic efficiency in the last periods of the mechanical systems is important to make effective maintenance decisions, ensure system reliability and availability, and decrease the overall cost. The proposed SC-BLSTM model is able to achieve more robust and effective prognostic prediction in the last stages.