The currently available deep neural network-based remaining useful life(RUL)prediction models are complex in struc-ture and do not meet the requirements of medium and long-term prediction tasks well.In order to make better use of temporal informa-tion,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.
关键词
机床齿轮箱轴承/时间卷积网络/时间序列/剩余寿命预测
Key words
gearbox bearings of machine tools/temporal convolutional network(TCN)/time series/remaining useful life(RUL)prediction
维普
万方数据