Metric-based domain adaptation meta-learning network for few-shot cross-domain fault diagnosis

Abstract

Abstract To address the challenges posed by significant distribution divergence between source and target domains and the scarcity of target samples in industrial equipment fault diagnosis, particularly under cross-condition and cross-platform scenarios with limited data, this paper proposes a Metric-based Meta Domain Adaptation Network (MMDAN). The proposed method integrates a Multi-scale Attention Residual Network (MARN), a domain adversarial mechanism, and a Meta-Siamese Network (MSN) to achieve deep feature extraction, cross-domain feature alignment, and rapid adaptation for accurate classification in few-shot learning settings. By incorporating multi-scale convolutions and a dual-attention mechanism, the feature representation capability is significantly enhanced. The purpose of introducing the domain discriminator is to train the feature extractor adversarially and thus improve the transfer robustness. Additionally, a task-driven meta-learning classifier with a Siamese structure is designed to mitigate issues of class imbalance and label scarcity. Experimental results on multiple industrial fault diagnosis datasets, including CWRU and RM, demonstrate that MMDAN outperforms existing methods in diagnostic accuracy and stability across various cross-domain transfer tasks. Notably, it shows strong generalization and adaptation capabilities even with extremely limited target samples, validating its broad applicability and effectiveness in real-world industrial scenarios.