Remaining useful life prediction of rolling bearings based on CNN-GRU-MSA with multi-channel feature fusion

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, only single channel or single-domain degradation information is considered to bear RUL prediction, and the prediction effect of existing RUL prediction methods is greatly limited. Therefore, in order to address the above issue, a novel method abbreviated as CNN-GRU-MSA 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 two benchmark datasets (i.e. IEEE PHM 2012 challenge datasets and XJTU-SY datasets). Experimental results demonstrate the superiority and effectiveness of the proposed method over other similar prediction methods in bearing RUL prediction.