Hierarchical Task-Building-Based Multisource Adaptive Meta Transfer Learning for Cross-Domain Fault Diagnosis

Cross-domain fault diagnosis leverages knowledge from multiple source domains to improve diagnostic accuracy in target domain. However, existing methods align source and target domains either jointly or independently, often neglecting the distributional discrepancies among source domains, which hinders effective knowledge transfer. To address this issue, we proposes a hierarchical task-building-based multisource adaptive meta transfer learning (HTB-MSAMTL) framework. First, the semantic alignment bidirectional embedding module pretrains meta-learning parameters through label-feature embedding alignment. Second, a task split strategy is designed for hierarchical meta-tasks, creating multiple domain pairs for learning long-term embeddable high-level meta-knowledge. Finally, a prototype feature reprojection network is developed to optimize calibrated prototypes and minimizes cross-domain distribution discrepancies through a differentiable closed-form solver. Moreover, learnable matrices replace fixed prototypes to enable adaptively tuned prototype representations. HTB-MSAMTL is evaluated on Tennessee Eastman Process, Case Western Reserve University, and Aluminum Electrolysis Process datasets. Results demonstrate HTB-MSAMTL outperforms existing methods in multisource cross-domain fault diagnosis under scarce labeled data scenarios.