Research on roller bearing fault diagnosis based on robust smooth constrained matrix machine under imbalanced data

Pan, Haiyang; Li, Bingxin; Zheng, Jinde; Tong, Jinyu; Liu, Qingyun; Deng, Shuchao

doi:10.1016/j.aei.2024.102667

Advanced Engineering Informatics

2024

DOI: 10.1016/j.aei.2024.102667

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Research on roller bearing fault diagnosis based on robust smooth constrained matrix machine under imbalanced data

Haiyang Pan,

Bingxin Li,

Jinde Zheng

et al.

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Cited by 3 publications

References 28 publications

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Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy

Wang,

Liang,

Bai

et al. 2025

Advanced Engineering Informatics

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Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy

Wang,

Liang,

Bai

et al. 2025

Advanced Engineering Informatics

View full text Add to dashboard Cite

Imbalanced fault diagnosis of a conditional variational auto-encoder with transfer and adversarial structures

Zhao,

Zhu,

Zhang

et al. 2025

Advanced Engineering Informatics

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Angle-based transfer support matrix machine for roller bearing fault diagnosis under limited labeled data

Tang,

Wang,

Pan

2024

Structural Health Monitoring

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In real-world fault diagnosis, the process of acquiring fault vibration signals frequently faces various challenges, which lead to certain fault types being represented by a scarcity of vibration signal data. Although support matrix machine (SMM) exhibits excellent classification ability in matrix-based data, its ideal model is difficult to establish with limited labeled samples. To address this limitation, a novel angle-based transfer support matrix machine (ATSMM) is proposed, which integrates matrix-based classifier and transfer learning theory. ATSMM employs an angle-based transfer learning method, which aims to reduce the angle between the normals of the target domain hyperplane and the source domain hyperplane, enabling the transfer of structural information across different domains. Meantime, ATSMM effectively addresses the issue of negative transfer that frequently occurs in transfer learning, which adds the weights calculated by the maximum mean discrepancy method to further improve the transfer effectiveness of the target domain model. The superiority of ATSMM is verified by two different roller bearing datasets, and the outcomes of experiments exhibit that the proposed ATSMM has excellent classification performance compared to alternative methods, especially in scenarios where the labeled sample size is limited.

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Research on roller bearing fault diagnosis based on robust smooth constrained matrix machine under imbalanced data

Cited by 3 publications

References 28 publications

Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy

Few-shot fault diagnosis for machinery using multi-scale perception multi-level feature fusion image quadrant entropy

Imbalanced fault diagnosis of a conditional variational auto-encoder with transfer and adversarial structures

Angle-based transfer support matrix machine for roller bearing fault diagnosis under limited labeled data

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