Cross-talk based multi-task learning for fault classification of machine system influenced by multiple variables

ArXi:2602.05146v2 Announce Type: replace-cross Machine systems inherently generate signals in which fault conditions and various variables influence signals measured from machine system. Although many existing fault classification studies rely solely on direct fault labels, the aforementioned signals naturally embed additional information shaped by other variables. Herein, we leverage this through a multi-task learning (MTL) framework that jointly learns fault conditions and other variables influencing measured signals.