Due to the complex and diverse operating conditions of rolling bearings, it is difficult to accurately predict remaining useful life (RUL) of rolling bearings via traditional prediction models. Besides, when a rolling bearing malfunctions, the degradation information contained in its collected full life data is distributed across multiple channels and domains, only single channel or single domain degradation information is considered for bearing RUL prediction, and the prediction effect of existing RUL prediction methods will be greatly limited. Therefore, to address the issue of single-channel and single-domain features inadequacy in reflecting the degradation process of rolling bearings comprehensively, a novel method abbreviated as CNN-GRU-MSA (multi-head self-attention) with multi-channel feature fusion is proposed for RUL prediction of rolling bearings. Firstly, the statistical features related to the time series are calculated and the similarity features are constructed based on the obtained statistical features. Then, the sensitive features are selected and fused through specific indicators. Finally, the dual-channel feature fusion is performed to construct a health indicator (HI), which is input into the proposed CNN-GRU-MSA model for training and achieving RUL prediction of rolling bearings. The effectiveness of the proposed method is validated using IEEE PHM 2012 challenge datasets. Experimental results demonstrate the superiority and effectiveness of the proposed method over other similar prediction methods in bearing RUL prediction.

remaining useful life prediction,feature fusion,health indicator,rolling bearings