Remaining useful life prediction method of bearings based on the interactive learning strategy

To address the issues of low accuracy, high time cost, and different failure modes in predicting the remaining useful life of rolling bearings, a remaining useful life prediction model based on an interactive learning convolution network with a feature attention mechanism (ILCANet) was proposed in this paper. The model divided time series data into an odd part and an even part, and the interactive learning strategy improved the ability of the model to extract features from long series. The binary tree structure was used to increase the number of network layers and to subdivide sequence features. In the model, residual connection was introduced to prevent the gradient from disappearing. In addition, in order to determine the degree of contribution of different features, a feature attention mechanism was embedded to assign weights to features. Experiments were conducted with PHM2012 and XJTU-SY datasets, and the results showed that the proposed method had better prediction accuracy in RUL prediction than other prediction methods.