LFA-MFNet: Local Feature-Aligned Multisource Information Fusion for Fault Diagnosis Under Limited and Imbalanced Conditions
Real-world data-driven fault diagnosis is constrained by scarce fault samples, limited and imbalanced (L&I) class distributions, and heterogeneous multisensor data under operating condition shifts, which impede stable and generalizable discriminative representation learning. We propose the local feature alignment multi...