A data-driven neural network approach for remaining useful life prediction

This paper proposed a neural network (NN) based remaining useful life (RUL) prediction approach. A new performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, back propagation neural networks are trained for RUL prediction, and average of the networks' outputs is considered as the final RUL in order to overcome prediction errors caused by random initiations of NNs. Finally, an experiment is set up based on a Bently-RK4 rotor unbalance test bed to validate the neural network based life prediction models, experimental results illustrate the effectiveness of the methodology.