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Research on rotating machinery fault feature extraction based on multi-scale improved differential filter
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Junchao GUO1, Qingbo HE2, Dong ZHEN3, Fengshou GU4
Journal of Vibration Engineering | 2025, 38(8) : 1747 - 1755
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Journal of Vibration Engineering | 2025, 38(8): 1747-1755
Research on rotating machinery fault feature extraction based on multi-scale improved differential filter
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Junchao GUO1, Qingbo HE2, Dong ZHEN3, Fengshou GU4
Affiliations
  • 1.School of Control Science and Engineering，Tiangong University，Tianjin 300387，China
  • 2.School of Mechanical Engineering，Shanghai Jiao Tong University，Shanghai 200240，China
  • 3.School of Mechanical Engineering，Hebei University of Technology，Tianjin 300130，China
  • 4.Centre for Efficiency and Performance Engineering，University of Huddersfield，Huddersfield HD1 3DH，UK
Published: 2025-08-10 doi: 10.16385/j.cnki.issn.1004-4523.202304021
Outline
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To accurately extract fault feature information under strong background noise，a multi-scale improved differential filter （MIDIF） is proposed for rotating machinery fault diagnosis. The rotating machinery vibration signal is decomposed into a series of multi-scale improved differential filter signals using MIDIF. In view of that the MIDIF filtered signals exhibit varying extents of validity in revealing fault features，a weighted reconstruction method using correlation analysis is proposed in which the weighted coefficients are counted and distributed to the corresponding MIDIF filtered signals to highlight the effective MIDIF filtered signals and weaken the invalid ones. The weighted coefficients are multiplied with the MIDIF filtered signals under different scales to produce transient impulse components. The fault types of rotating machines are inferred from the fault defect frequencies in the envelope spectrum of the transient impulses. The results show that MIDIF is more accurate in extracting fault features than multi-scale average combination different morphological filter （ACDIF） and multi-scale morphology gradient product operation （MGPO），and that it provides an effective method for rotating machinery fault diagnosis.

multi-scale improved differential filter  /  correlation coefficients  /  rotating machinery  /  fault diagnosis
Junchao GUO, Qingbo HE, Dong ZHEN, Fengshou GU. Research on rotating machinery fault feature extraction based on multi-scale improved differential filter[J]. Journal of Vibration Engineering, 2025 , 38 (8) : 1747 -1755 . DOI: 10.16385/j.cnki.issn.1004-4523.202304021
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Year 2025 volume 38 Issue 8
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.202304021
  • Receive Date：2023-04-17
  • Online Date：2026-02-09
  • Published：2025-08-10
Article Data
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History
  • Received：2023-04-17
  • Revised：2023-07-03
Funding
Affiliations
    1.School of Control Science and Engineering，Tiangong University，Tianjin 300387，China
    2.School of Mechanical Engineering，Shanghai Jiao Tong University，Shanghai 200240，China
    3.School of Mechanical Engineering，Hebei University of Technology，Tianjin 300130，China
    4.Centre for Efficiency and Performance Engineering，University of Huddersfield，Huddersfield HD1 3DH，UK
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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
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Genus		 种数
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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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