You, Peijie, Wang, Lei, Nguyen, Anh 
ORCID: 0000-0002-1449-211X, Zhang, Xin ORCID: 0000-0003-4236-7436 and Huang, Baoru
(2026)
Channel-adaptive generative reconstruction and fusion for multi-sensor graph features in few-shot fault diagnosis.
Information Fusion, 127.
p. 103742.
ISSN 1566-2535
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Text
1-s2.0-S1566253525008048-main.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (10MB) | Preview |
Abstract
Recently, multi-sensor feature fusion has been proven to be an effective strategy for improving the accuracy of few-shot fault diagnosis. However, existing fault diagnosis models based on multi-sensor feature fusion often overlook significant inter-channel discrepancies and struggle to mitigate noise pollution inherent in multi-source signals. To address these limitations, this paper proposes a channel-adaptive generative reconstruction and fusion framework that integrates a contrastive variational graph autoencoder feature fusion (CogFusion) module for robust few-shot fault representation learning. The CogFusion module leverages the generative capability of a contrastive variational graph autoencoder (CGE) to reconstruct noise-suppressed node features while explicitly modeling latent distributions of multi-sensor signals. By incorporating a multi-channel parallel graph contrastive learning strategy, CogFusion enhances discriminative feature separation by contrasting topological structures of positive and negative sample pairs, effectively isolating fault-related patterns from noisy embeddings. To adaptively fuse multi-channel information, a channel discrepancy-guided weighting mechanism dynamically prioritizes high-credibility sensor features, mitigating the impact of low-quality data. To further enhance feature learning in few-shot diagnosis, a dual-scale topological Transformer (DSTT) model is introduced to deeply mine the reconstructed multi-channel topological graph, enabling high-precision few-shot fault diagnosis. Experimental results on the axial flow pump and HUSTgearbox datasets demonstrate that the proposed method outperforms both single-channel and existing multi-sensor feature fusion methods, highlighting its superiority in feature fusion and cross-channel information integration.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 4605 Data Management and Data Science, 46 Information and Computing Sciences, Networking and Information Technology R&D (NITRD), 1.1 Normal biological development and functioning |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Computer Science & Informatics Faculty of Science & Engineering > School of Computer Science & Informatics > Artificial Intelligence |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 03 Nov 2025 08:33 |
| Last Modified: | 03 Nov 2025 08:33 |
| DOI: | 10.1016/j.inffus.2025.103742 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3195157 |
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