Multi-Target Adversarial Learning for Partial Fault Detection Applied to Electric Motor-Driven Systems

Deep neural network-based fault diagnosis is gaining significant attention within the Industry 4.0 framework, yet practical deployment is still hindered by domain shift, partial label mismatch, and class imbalance. In this regard, this paper proposes a Multi-Target Adversarial Learning for Partial Fault Diagnosis (MTAL-PFD), an extension of adversarial and discrepancy-based domain adaptation tailored to single-source, multi-target (1SmT) partial fault diagnosis in electric motor-driven systems. The framework transfers knowledge from a labeled source to multiple unlabeled target domains by combining dual 1D-CNN feature extractors with adversarial domain discriminators, an inconsistency-based regularizer to stabilize learning, and class-aware weighting to mitigate partial label shift by down-weighting outlier source classes. Thus, the proposed scheme combines a multi-objective approach with partial domain adaptation applied to the diagnosis of electric motor-driven systems. The proposed model is evaluated across 24 cross-domain tasks and varying operating conditions on two motor test benches, showing consistent improvements over representative baselines.