The currently available deep neural network-based remaining useful life（RUL） prediction models are complex in structure and do not meet the requirements of medium and long-term prediction tasks well.In order to make better use of temporal information,a temporal convolutional network (TCN) based RUL prediction model for bearings was designed.Taking the spectral features of the vibration signal as input,a causally inflated convolutional structure was used to extract the frequency domain features and capture the long-term dependence to achieve accurate prediction of the bearing RUL.To further illustrate the superiority of the proposed method,comparative experiments were conducted using a convolutional neural network (CNN) and a gated recurrent unit (GRU).The results show that the RUL prediction accuracy of the proposed TCN model outperforms other existing methods with high accuracy.