Multi-task dual-level adversarial transfer learning boosted RUL estimation of CNC milling tools

Abstract

Effectively estimating the remaining useful life (RUL) of milling tools is crucial for intelligent preventive maintenance of CNC milling systems. In this paper, a novel generalized RUL estimation model based on multi-task dual-level adversarial transfer learning with multi-level attention (MTDTL-MA) is proposed for tool RUL prediction with variable working conditions. A multi-task learning structure with multi-level attention is used to predict the wear of each tool face in parallel and capture the max wear of entire tools as a health index for more accurate RUL estimation. Multi-channel encoder-decoder self-attention, multi-gate attention and global-local adversarial transferable attention are integrated to emphasize useful wear-related features, tool face-specific features and transferable features between source and target domains, respectively. A new auxiliary subdomain adversarial domain adaptation and global-local adversarial transferable attention is proposed to form a dual-level adversarial domain adaptation to synergistically improve transfer learning. Both the PHM2010 and Ideahouse dataset (2021) are employed to verify the effectiveness of MTDTL-MA, and the results indicate that the proposed method provides higher RUL prediction accuracy compared to several state-of-the-art methods.