Remaining useful life prediction based on parallel multi-scale feature fusion network

Abstract

In the domain of Predictive Health Management (PHM), the prediction of Remaining Useful Life (RUL) is pivotal for averting machinery malfunctions and curtailing maintenance expenditures. Currently, most RUL prediction methods overlook the correlation between local and global information, which may lead to the loss of important features and, consequently, a subsequent decline in predictive precision. To address these limitations, this study presents a groundbreaking deep learning framework termed the Parallel Multi-Scale Feature Fusion Network (PM2FN). This approach leverages the advantages of different network structures by constructing two distinct feature extractors to capture both global and local information, thereby providing a more comprehensive feature set for RUL prediction. Experimental results on two publicly available datasets and a real-world dataset demonstrate the superiority and effectiveness of our method, offering a promising solution for industrial RUL prediction.