Global-focal Adaptation with Information Separation for Noise-robust Transfer Fault Diagnosis

• URL: http://arxiv.org/abs/2510.16033v1
• Date: Thu, 16 Oct 2025 01:13:06 GMT
• Title: Global-focal Adaptation with Information Separation for Noise-robust Transfer Fault Diagnosis
• Authors: Junyu Ren, Wensheng Gan, Guangyu Zhang, Wei Zhong, Philip S. Yu,
• Abstract summary: We propose an information separation global-focal adversarial network (ISGFAN) for cross-domain fault diagnosis under noise conditions.<n>ISGFAN is built on an information separation architecture that integrates adversarial learning with an improved loss to decouple domain-invariant fault representation.<n>Experiments conducted on three public datasets demonstrate that the proposed method outperforms other prominent existing approaches.
• Score: 48.69961294481149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing transfer fault diagnosis methods typically assume either clean data or sufficient domain similarity, which limits their effectiveness in industrial environments where severe noise interference and domain shifts coexist. To address this challenge, we propose an information separation global-focal adversarial network (ISGFAN), a robust framework for cross-domain fault diagnosis under noise conditions. ISGFAN is built on an information separation architecture that integrates adversarial learning with an improved orthogonal loss to decouple domain-invariant fault representation, thereby isolating noise interference and domain-specific characteristics. To further strengthen transfer robustness, ISGFAN employs a global-focal domain-adversarial scheme that constrains both the conditional and marginal distributions of the model. Specifically, the focal domain-adversarial component mitigates category-specific transfer obstacles caused by noise in unsupervised scenarios, while the global domain classifier ensures alignment of the overall distribution. Experiments conducted on three public benchmark datasets demonstrate that the proposed method outperforms other prominent existing approaches, confirming the superiority of the ISGFAN framework. Data and code are available at https://github.com/JYREN-Source/ISGFAN

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