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This document presents a novel multi-task learning framework for remaining useful life (RUL) estimation of mechanical systems, focusing on fault mode (FM) feature separation. The proposed Self-Attention Capsule Network (SA-CapsNet) utilizes an unsupervised autoencoder for FM clustering and employs multi-task parallel learning to enhance RUL prediction accuracy by effectively utilizing FM information. Experimental results demonstrate the framework

Author

lakec9639

Language

EN