Deep Recurrent Convolutional Neural Network for Remaining Useful Life Prediction

Abstract

Remaining Useful Life (RUL) prediction of rotating machinery plays a critical role in Prognostics and Health Management (PHM). Data-driven methods for RUL estimation have been widely developed because they don’t depend on much prior knowledge of the system. Recurrent neural network (RNN) is capable of modeling sequential data, which has been investigated for RUL prediction with statistical features of vibration signals in time domain and frequency domain. The drawback of utilizing statistical features is the ignorance of time-frequency information, which is critical in RUL prediction because the vibration signals are non-stationary when the fault occurs. To solve this problem, a novel deep architecture, named deep recurrent convolutional neural network (DRCNN) is proposed. By incorporating convolutional operation in the process of state transition of RNN, the spatial information in time-frequency domain can be automatically learned from the vibration signals, which contributes to the improvement of prediction performance. With convolutional operation in RNN, both spatial information in time-frequency domain and previous information are employed for RUL prediction. Furthermore, by stacking recurrent convolutional neural network layer by layer, the deep architecture can learn high-level features in the time-frequency domain. Finally, experimental analysis of RUL prediction using vibration signals of run-to-failure tests are carried out. Compared with the results of conventional deep RNN method, the proposed method shows its effectiveness and superiority.