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

•A new core building block, i.e., recurrent convolutional layer, is built to learn the temporal dependencies from time-series sensor data, which enables the prognostics network to effectively memorize useful degradation information over time and thus enhances its representation ability.•A probabilistic RUL prediction result is obtained based on variational inference, which breaks the inherent limitation of convolutional neural networks and is beneficial to maintenance decision making.•A systematic prognostics framework named recurrent convolutional neural network is proposed for RUL prediction of machinery, and its effectiveness and superiority are verified by two case studies, i.e., RUL prediction of rolling element bearings and RUL prediction of milling cutters.