Multi-source partial domain adaptation method based on pseudo-balanced target domain for fault diagnosis

Multisource domain adaptation (MSDA) for fault diagnosis enables knowledge transfer from multisource domains to unknown target domains, which is crucial for enhancing equipment reliability and safety. However, current MSDA methods face two major challenges: (1) they face serious domain-shift problems not only between the target and source domains, but also between different source domains, and (2) in practical applications, class labels in the target domain are likely to be subsets of class labels in the source domain. Therefore, this study considers a setting called multisource partial domain adaptation (MSPDA). A two-stage intelligent fault diagnosis method for MSPDA based on pseudo-balanced target domains (PBTD) is proposed. In the first stage, we propose a weighted adversarial partial domain adaptation method based on a dual progressive strategy that aligns each source domain with the target domain to construct a series of PBTD. In the second stage, an alternating learning scheme is utilized to align the remaining source domains with the PBTDs, which makes full use of multisource information to bridge the discrepancies between different domains. Additionally, this study proposes a multiscale convolutional neural network based on a three-branch attention mechanism that captures the cross-dimensional interactions of scale, channel, and space to enhance the algorithmic characterization capability. Finally, extensive experiments with gear and bearing datasets are conducted to validate the effectiveness of the proposed method.