Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery

Abstract Deep learning is becoming more appealing in remaining useful life (RUL) prediction of machines, because it is able to automatically build the mapping relationship between the raw data and the corresponding RUL by representation learning. Among deep learning models, convolutional neural networks (CNNs) are gaining special attention because of its powerful ability in dealing with time-series signals, and have achieved promising results in current studies. These studies, however, suffer from the two limitations: (1) The temporal dependencies of different degradation states are not considered during network construction; and (2) The uncertainty of RUL prediction results cannot be obtained. To overcome the above-mentioned limitations, a new framework named recurrent convolutional neural network (RCNN) is proposed in this paper for RUL prediction of machinery. In RCNN, recurrent convolutional layers are first constructed to model the temporal dependencies of different degradation states. Then, variational inference is used to quantify the uncertainty of RCNN in RUL prediction. The proposed RCNN is evaluated using vibration data from accelerated degradation tests of rolling element bearings and sensor data from life testing of milling cutters, and compared with some state-of-the-art prognostics approaches. Experimental results demonstrate the effectiveness and superiority of RCNN in improving the accuracy and convergence of RUL prediction. More importantly, RCNN is able to provide a probabilistic RUL prediction result, which breaks the inherent limitation of CNNs and facilitates maintenance decision making.