Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network

Remaining useful life (RUL) prediction is very important for improving the availability of a system and reducing its life cycle cost. This paper proposes a deep long short-term memory (DLSTM) network-based RUL prediction method using multiple sensor time series signals. The DLSTM model fuses multi-sensor monitoring signals for accurate RUL prediction, which is able to discover the hidden long-term dependencies among sensor time series signals through deep learning structure. By grid search strategy, the network structure and parameters of the DLSTM are efficiently tuned using an adaptive moment estimation algorithm so as to realize an accurate and robust prediction. Two various turbofan engine datasets are adopted to verify the performance of the DLSTM model. The experimental results demonstrate that the DLSTM model has a competitive performance in comparison with state-of-the-arts reported in literatures and other neural network models. •A new deep long short-term memory (DLSTM) model is constructed for accurate remaining useful life (RUL) prediction.•Proposed DLSTM model fuses multi-sensor signals for enhanced RUL prediction performance.•The proposed method is very suitable for multisensory scenario which is validated by two multisensory experiments.