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A spectral ensemble sparse representation classification model-driven super-robust intelligent diagnostic method
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Yun KONG1, 2, 3, Guoyu HUANG1, Mingming DONG1, Ke CHEN1, 5, Hui LIU1, Fulei CHU4
Journal of Vibration Engineering | 2025, 38(6) : 1242 - 1251
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Journal of Vibration Engineering | 2025, 38(6): 1242-1251
A spectral ensemble sparse representation classification model-driven super-robust intelligent diagnostic method
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Yun KONG1, 2, 3, Guoyu HUANG1, Mingming DONG1, Ke CHEN1, 5, Hui LIU1, Fulei CHU4
Affiliations
  • 1.School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
  • 2.Tangshan Research Institute,Beijing Institute of Technology,Tangshan 063015,China
  • 3.State Key Laboratory of Mechanical Transmission for Advanced Equipment,Chongqing University,Chongqing 400044,China
  • 4.Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China
  • 5.Inner Mongolia First Machinery Group Co., Ltd., Baotou 014032, China
Published: 2025-06-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.06.012
Outline
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Under the background of industrial big data and intelligent manufacturing, data-driven intelligent fault diagnosis technology has become a crucial enabling technology. It ensures the safe and reliable operation of high-end equipment, facilitates equipment health management, and supports intelligent operation and maintenance. Existing intelligent fault diagnosis models often fail to simultaneously achieve superior diagnostic accuracy, strong noise immunity, high computational efficiency, and robust hyperparameter performance. To address these limitations, this paper proposes a novel spectral ensemble sparse representation classification model-driven super-robust intelligent diagnostic method. The proposed method designs a vibration data augmentation strategy based on cascade segmentation operators, aiming to enhance both the quantity and quality of vibration data samples. It utilizes the spectral features of vibration signals for dictionary atom design and constructs a spectral ensemble dictionary design strategy that incorporates spectral feature fusion. This improves the reconstruction capability of the spectral sparse representation dictionary. The method develops an intelligent recognition strategy based on the spectral sparse approximation error minimization criterion to achieve intelligent diagnosis of test samples health status. The proposed method is validated on a planetary gear transmission fault dataset. Results demonstrate that the intelligent diagnosis method can integrate the advantages of superior diagnostic accuracy, strong noise immunity, high computational efficiency, and robust hyperparameter selection. Its diagnosis results surpass existing advanced methods, showcasing significant application for data-driven intelligent fault diagnosis of industrial equipment.

fault diagnosis  /  data-driven  /  sparse representation  /  spectral ensemble dictionary
Yun KONG, Guoyu HUANG, Mingming DONG, Ke CHEN, Hui LIU, Fulei CHU. A spectral ensemble sparse representation classification model-driven super-robust intelligent diagnostic method[J]. Journal of Vibration Engineering, 2025 , 38 (6) : 1242 -1251 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.06.012
Year 2025 volume 38 Issue 6
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.06.012
  • Receive Date:2024-06-21
  • Online Date:2026-02-12
  • Published:2025-06-10
Article Data
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History
  • Received:2024-06-21
  • Revised:2024-09-27
Funding
Affiliations
    1.School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    2.Tangshan Research Institute,Beijing Institute of Technology,Tangshan 063015,China
    3.State Key Laboratory of Mechanical Transmission for Advanced Equipment,Chongqing University,Chongqing 400044,China
    4.Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China
    5.Inner Mongolia First Machinery Group Co., Ltd., Baotou 014032, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
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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