Dual-dimensional adaptive sequential learning: Enhanced fault diagnosis under multivariate data missing

As data environments become more complex, defects in sensors present significant challenges for fault diagnosis, particularly under multivariate data missing (MDM). We propose a dual-dimensional adaptive sequential learning framework addressing two key aspects: (1) Data complexity quantification via generative adversarial imputation network (GAIN) and Gaussian mixture model (GMM) clustering, generating subsets stratified by incompleteness (measured by MDM complexity); (2) Multi-teacher knowledge distillation where a student model progressively learns from teachers, with supervision dynamically weighted by confidence scores. Inspired by human learning dynamics, this dual strategy integrates sample sequencing (curriculum difficulty) and supervision intensity (teacher dependency). Validation on satellite power systems confirms >95 % diagnosis accuracy for mixed MDM data, outperforming nonsequential methods in accuracy and generalizability.