Prediction of remaining useful life of turbofan engine based on optimized model
Yuefeng Liu, Xiaoyan Zhang, Wei Guo, Haodong Bian, Yingjie He, Zhen Liu
Abstract
To realize the prognostics and health management (PHM) of the mechanical system, it is the key to accurately predict the remaining useful life (RUL) of the equipment. The network captured features at different time steps will contribute to the final RUL prediction to varying degrees. Therefore, a deep learning network based on the attention mechanism is proposed. Firstly, the raw sensor data is passed to the Bi-LSTM network to capture the long-term dependence of features. Secondly, the features extracted by Bi-LSTM are passed to the attention mechanism for feature weighting, thereby giving greater weight to important features. Finally, the weighted features are input into the fully connected network to predict the RUL of the turbofan engine. Using the data set C-MAPSS to explore the feasibility of this method. The results show that this method is more accurate than other RUL prediction methods.