In order to effectively describe the performance degradation trend of rolling bearing and accurately predict its remaining useful life,a method for predicting the remaining useful life of rolling bearings based on multi-domain characteristics fusion in Transformer-GRU parallel network was proposed.An evaluation index was established to screen the sensitive features for the time domain,frequency domain,and time-frequency domain of the rolling bearing vibration signal,and the sensitive features with high scores were obtained and the degraded feature set was obtained.The degradation feature set dimension was reduced by using self-coding to reduce data complexity and redundancy,and the degradation curve of rolling bearing was obtained.Finally,the remaining useful life prediction was carried out using Transformer-GRU parallel network,and the method was applied to the analysis of the public bearing dataset.The results show that the Transformer-GRU parallel network can not only capture long-term dependencies in input sequences efficiently and accurately,but also process features between time series better.Compared with LSTM,GRU and other classical methods,the proposed method can effectively predict the remaining useful life of rolling bearings.