Semantic Feature Segmentation for Interpretable Predictive Maintenance in Complex Systems

ArXi:2605.14318v1 Announce Type: cross Predictive maintenance in complex systems is often complicated by the heterogeneity and redundancy of monitored variables,which can obscure fault-relevant information and reduce model interpretability. This work proposes a semantic feature segmentation framework that decomposes the monitored feature space into a canonical component,expected to retain the dominant predictive information, and a residual component containing structurally peripheral signals.