基于卷积自编码与密集时间卷积网络的回转支承退化趋势预测

Here, to accurately predict the health indicator reflecting performance degradation of slewing support, a degradation trend prediction model based on the improved temporal convolution network (TCN) called the densely temporal convolution network (DTCN) was proposed. DTCN drew lessons from the Dense-block module in Dense-Net network to improve its own network structure, and solve problems of the loss function of TCN dropping slowly in training, its network being not easy to converge and poor convergence effect. Then, the whole life-cycle test data of slewing support were used, the health indicator was established with help of the convolutional auto-encoders (CAE) and the hidden Markov model (HMM), and the effectiveness of this improved algorithm was verified. Finally, DTCN was compared with other series prediction models, such as, the long-short term memory (LSTM) network and the gated recurrent unit (GRU) network. The results showed that the proposed model has advantages in prediction effect; it can more accurately predict changes of the health indicator; it can be used to predict degradation trend of slewing support.