Remaining useful life prediction for multi-sensor systems using a novel end-to-end deep-learning method

Remaining useful life (RUL) prediction plays a crucial role in ensuring reliability and safety of modern engineering systems. For complicated systems, the indirect manner of the conventional RUL prediction approaches restricts their universality and accuracy. The challenge to realize accurate RUL estimation consists in the direct exploration of the potential relationship between the RUL and the numerous data from multiple monitoring sensors. Motivated by this fact, a novel end-to-end RUL prediction method is proposed based on a deep learning model in this paper. The long short-term memory (LSTM) encoder-decoder is employed as the main frame of the model to deal with multivariate time series data. Then a two-stage attention mechanism is developed to realize adaptive extraction and evaluation of the input features and temporal correlation. On this basis, the RUL prediction is obtained by a multilayer perceptron. The proposed model can selectively focus on the critical information without any prior knowledge, which is of great significance to enhance the RUL prediction accuracy. The effectiveness and superiority of the proposed method is experimentally validated through a turbofan engine dataset and compared with the state-of-the-art methods.