[2602.05146] Cross-talk based multi-task learning for fault classification of machine system influenced by multiple variables

Computer Science > Machine Learning arXiv:2602.05146 (cs) [Submitted on 5 Feb 2026 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Cross-talk based multi-task learning for fault classification of machine system influenced by multiple variables Authors:Wonjun Yi, Rismaya Kumar Mishra, Yong-Hwa Park View a PDF of the paper titled Cross-talk based multi-task learning for fault classification of machine system influenced by multiple variables, by Wonjun Yi and 2 other authors View PDF Abstract: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. Among MTL architectures, cross-talk structures have distinct advantages because they allow for controlled information exchange between tasks through the cross-talk layer while preventing negative transfer, in contrast to shared trunk architectures that often mix incompatible features. We build on our previously introduced residual neural dimension reductor model, and extend its application to two benchmarks where system influenced by multiple variables. The first benchmark is a drone fault dataset, in which m...