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Fault Diagnosis Method of Rotating Machinery Based on Unsupervised Cross-modal Euler Discriminant Space
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Jian CHEN1, Shu-zhi SU1, Yan-min ZHU2, *
Science Technology and Engineering | 2025, 25(11) : 4621 - 4628
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Science Technology and Engineering | 2025, 25(11): 4621-4628
• Papers·Automation and Computational Technology •
Fault Diagnosis Method of Rotating Machinery Based on Unsupervised Cross-modal Euler Discriminant Space
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Jian CHEN1, Shu-zhi SU1, Yan-min ZHU2, *
Affiliations
  • 1 School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
  • 2 School of Mechanical and Electrical Engineering, Anhui University of Science & Technology, Huainan 232001, China
Published: 2025-04-18 doi: 10.12404/j.issn.1671-1815.2403630
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The high-precision fault diagnosis of cross modal high-dimensional fault data under unsupervised conditions is a challenging problem. To address this issue, a rotating machinery fault diagnosis method based on unsupervised cross-modal Euler discriminant space (UCEDS) was proposed. In this method, cross-modal fault data samples were mapped to Euler representations through cosine metrics to enhance the differences and separability between different types of fault samples. Then, an unsupervised cross modal Euler discriminant space learning model was constructed in this space, and the analytical solution of the model was theoretically derived. This model not only considered the local neighborhood structure of fault samples, but also effectively discovered the local structural information of complex and nonlinear fault feature samples. At the same time, on the basis of cross modal consistent discriminative fusion, it further improved the complementarity between low dimensional discriminative feature subsets. Targeted experiments on the Paderborn fault bearing dataseht showed that the proposed UCEDS method had superior fault diagnosis and classification performance.

rolling bearing  /  fault diagnosis  /  cross-modal  /  Euler representation  /  dimension reduction
Jian CHEN, Shu-zhi SU, Yan-min ZHU. Fault Diagnosis Method of Rotating Machinery Based on Unsupervised Cross-modal Euler Discriminant Space[J]. Science Technology and Engineering, 2025 , 25 (11) : 4621 -4628 . DOI: 10.12404/j.issn.1671-1815.2403630
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Year 2025 volume 25 Issue 11
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Article Info
doi: 10.12404/j.issn.1671-1815.2403630
  • Receive Date：2024-05-16
  • Online Date：2025-07-09
  • Published：2025-04-18
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  • Received：2024-05-16
  • Revised：2024-08-01
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Affiliations
    1 School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
    2 School of Mechanical and Electrical Engineering, Anhui University of Science & Technology, Huainan 232001, China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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