Remaining useful life prediction of machinery based on improved Sample Convolution and Interaction Network

Abstract

Remaining useful life (RUL) prediction is significant in ensuring the safe and reliable operation of machinery and reducing maintenance costs. There are currently numerous deep learning-based methods for machinery RUL prediction. However, some studies overlook the differences in the contributions of data from different sensors or different time points of the same sensor, and most research only extracts information from feature or sequence dimensions, which inevitably affects the efficiency and accuracy of RUL prediction. Therefore, we proposed a method based on a multi-dimensional attention mechanism and feature-sequence dimensional sample convolution and interaction network (MFSSCINet) to predict the machinery RUL effectively, which includes a Feature-Sequence Dimension Attention Module to capture information interactions in feature dimension and learn the impact weights of various time steps in sequence dimension. Then, a Multi-Source Information Fusion Module was constructed to extract helpful information from features of different dimensions and time resolutions and fuse them. Finally, a RUL Prediction Module was built to estimate the machine RUL effectively. The method’s effectiveness is validated in C-MAPSS and XJTU-SY datasets. Experimental results show that the MFSSCINet model has higher accuracy in machine RUL prediction tasks than other advanced computational methods.