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Compound fault diagnosis of rolling bearing based on AVME-OMOMEDA
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Zhijun LIU1, Jun ZHOU1, 2, Xing WU1, 2, Tao LIU1, 2
Journal of Vibration Engineering | 2025, 38(9) : 2130 - 2140
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Journal of Vibration Engineering | 2025, 38(9): 2130-2140
Compound fault diagnosis of rolling bearing based on AVME-OMOMEDA
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Zhijun LIU1, Jun ZHOU1, 2, Xing WU1, 2, Tao LIU1, 2
Affiliations
  • 1.Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China
  • 2.Advanced Equipment Intelligent Manufacturing Technology of Yunnan Key Laboratory, Kunming 650500, China
Published: 2025-09-10 doi: 10.16385/j.cnki.issn.1004-4523.202310059
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Traditional algorithms are difficult to effectively separate and extract the composite fault features of bearings with overlapping resonance bands, an adaptive rolling bearing composite fault feature separation and extraction method combining adaptive variational mode extraction (AVME) and optimized multi-point optimal minimum entropy deconvolution adjusted (OMOMEDA) is proposed in this paper. The initial value of the center frequency of the VME parameter is determined by using the autocorrelation energy spectrum of S transform spectrum, and the desired modes related to the fault are extracted. Then the desired modes are linearly superimposed to reconstruct the original signal to realize the noise reduction of the signal. Extract periodic pulse signals from the reconstructed signal using OMOMEDA, and obtain fault characteristic frequencies by combining with envelope demodulation. The simulation and test signals verify that the method can effectively separate and extract the composite fault features of bearings with overlapping resonance bands. And compared with four other existing algorithms such as VMD-MCKD, the superiority of the proposed method is demonstrated.

fault diagnosis  /  rolling bearing  /  adaptive variational mode extraction  /  optimized multipoint optimal minimum entropy deconvolution  /  autocorrelation energy spectrum of S transform spectrum
Zhijun LIU, Jun ZHOU, Xing WU, Tao LIU. Compound fault diagnosis of rolling bearing based on AVME-OMOMEDA[J]. Journal of Vibration Engineering, 2025 , 38 (9) : 2130 -2140 . DOI: 10.16385/j.cnki.issn.1004-4523.202310059
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Year 2025 volume 38 Issue 9
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.202310059
  • Receive Date：2023-10-25
  • Online Date：2026-02-09
  • Published：2025-09-10
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History
  • Received：2023-10-25
  • Revised：2023-12-29
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Affiliations
    1.Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China
    2.Advanced Equipment Intelligent Manufacturing Technology of Yunnan Key Laboratory, Kunming 650500, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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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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