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Period-refined maximum correlated kurtosis deconvolution method for weak fault feature extraction in rolling bearings
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Yonghao MIAO1, 2, Huifang SHI1, Chenhui LI1, Xiaohui GU2
Journal of Vibration Engineering | 2025, 38(6) : 1317 - 1325
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Journal of Vibration Engineering | 2025, 38(6): 1317-1325
Period-refined maximum correlated kurtosis deconvolution method for weak fault feature extraction in rolling bearings
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Yonghao MIAO1, 2, Huifang SHI1, Chenhui LI1, Xiaohui GU2
Affiliations
  • 1.School of Reliability and Systems Engineering,Beihang University,Beijing 100191,China
  • 2.State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang Tiedao University,Shijiazhuang 050043,China
Published: 2025-06-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.06.020
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Maximum correlated kurtosis deconvolution (MCKD), which uses correlated kurtosis as its deconvolution target, effectively extracts both periodic and impulsive features of mechanical faults. This is a widely used method for solving rolling bearing fault diagnosis problems. However, the performance of MCKD heavily relies on accurate prior fault period information. Existing solution often only focus on period estimation during the iterative process, making them ineffective under low signal-to-noise ration (SNR) conditions. To address this limitation, a period-refined maximum corrlated kurtosis deconvolution (PRMCKD) method is proposed. This approach refines the iteration period using time synchronous averaging (TSA) for reconolution, enabling accurate extraction of subtle bearing fault features even in strong noise environments. The method operates by first utilizing a filter bank for preliminary localization of the resonance frequency band, thus defining the correct deconvolution direction. With correlated kurtosis as the objective function, and leveraging the period information refined by TSA technology, the optimal filter coefficients are iteratively solved. Rolling bearing fault localization is achieved through the fault features present in the filtered signal. Simulation and experimental analysis results demonstrate that the proposed PRMCKD method offers significant advantages over traditional deconvolution methods for extracting weak fault features in rolling bearings.

rolling bearing  /  fault diagnosis  /  time synchronous averaging  /  initialization filter  /  deconvolution
Yonghao MIAO, Huifang SHI, Chenhui LI, Xiaohui GU. Period-refined maximum correlated kurtosis deconvolution method for weak fault feature extraction in rolling bearings[J]. Journal of Vibration Engineering, 2025 , 38 (6) : 1317 -1325 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.06.020
Year 2025 volume 38 Issue 6
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.06.020
  • Receive Date:2024-01-26
  • Online Date:2026-02-12
  • Published:2025-06-10
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  • Received:2024-01-26
  • Revised:2024-05-30
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    1.School of Reliability and Systems Engineering,Beihang University,Beijing 100191,China
    2.State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures,Shijiazhuang Tiedao University,Shijiazhuang 050043,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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