Multisource Importance-Based Hierarchical Adaptation Network for Cross-Domain Fault Diagnosis

Multisource domain adaptation aims to transfer knowledge from multiple source domains to improve the generalization performance of fault diagnosis. However, most existing approaches treat all source domains equally, which may cause negative transfer by ignoring interdomain discrepancies, when some source domains are less relevant to the target domain. To address this, this article proposes a multisource importance-based hierarchical adaptation network (MSIHAN) for cross-domain fault diagnosis. MSIHAN employs a domain importance recognition module to estimate the probability that each target sample belongs to each source domain. Based on the estimated importance, weighted moment matching and weighted class-aware distribution alignment are presented to achieve marginal and conditional distribution alignment by prioritizing source domains with higher importance. In addition, the weighted classification loss is introduced to constrain the feature generator to generate aligned and distinguishable representations. Extensive experiments demonstrate the effectiveness of MSIHAN, with comparative results highlighting its superiority over mainstream methods.