A novel bootstrap ensemble learning convolutional simple recurrent unit method for remaining useful life interval prediction of turbofan engines

Abstract The deep neural network is widely applied in remaining useful life (RUL) prediction because of its strong feature extraction ability. However, the prediction results of deep learning neural networks are often influenced by random noise and modeling parameters. Moreover, the training process of the traditional neural network is time-consuming. To overcome these drawbacks, a novel bootstrap ensemble learning convolutional simple recurrent unit (ELCSRU) method is proposed for RUL prediction. The simple recurrent unit is used to learn the time-series features of sensor data, which can effectively reduce the model parameters and boost the calculation speed. Moreover, the RUL prediction uncertainty can be quantified using the prediction interval, which can be calculated by the ELCSRU model. The prediction performance of the ELCSRU model is demonstrated using a turbofan engine dataset. The experimental results show that the proposed ELCSRU model provides a prognosis framework with better prediction performance for quantifying RUL prediction uncertainty.