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Research on diagnosis and identification of acoustic emission signals for non-contact rotary seal failures
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Jinxin CHEN1, 2, Junjie LU1, Xuexing DING2, Jie XU2, Junhua DING2, De GAO1
Journal of Vibration Engineering | 2025, 38(10) : 2369 - 2377
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Journal of Vibration Engineering | 2025, 38(10): 2369-2377
Research on diagnosis and identification of acoustic emission signals for non-contact rotary seal failures
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Jinxin CHEN1, 2, Junjie LU1, Xuexing DING2, Jie XU2, Junhua DING2, De GAO1
Affiliations
  • 1.Ningbo Key Laboratory of Advanced Seal Technology, University of NingboTech, Ningbo 315000, China
  • 2.School of Petrochemical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
doi: 10.16385/j.cnki.issn.1004-4523.202307046
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To address the issue of unclear and challenging identification of non-contact rotating seal fault signals, this study established an experimental platform and acoustic emission testing system. It involved monitoring acoustic emission signals during various operational conditions, including normal operation and six typical fault scenarios of non-contact rotating seals. A total of 14000 feature samples were effectively collected. By applying the Bayesian optimization algorithm and incorporating continuous wavelet transform, an adaptive convolutional neural network classification model was constructed. Subsequently, the diagnostic performance of the fault recognition model was analyzed using confusion matrices and t-distributed stochastic neighbor embedding. The research results demonstrate that this model successfully classifies and identifies seven different operational conditions of non-contact rotating seals, including normal operation, dry friction, mixed lubrication, spring failure, end-face pitting, local spring failure, and end-face scratching, with an average recognition accuracy of 99.7023%. This achievement underscores the capability of effectively isolating and identifying seal fault sources from acoustic emission signals of non-contact rotating seals in non-stationary, complex, and overlapping environments, thereby establishing a solid theoretical foundation for practical engineering applications.

fault diagnosis  /  acoustic emission  /  non-contact rotary seal  /  condition recognition  /  deep learning
Jinxin CHEN, Junjie LU, Xuexing DING, Jie XU, Junhua DING, De GAO. Research on diagnosis and identification of acoustic emission signals for non-contact rotary seal failures[J]. Journal of Vibration Engineering, 2025 , 38 (10) : 2369 -2377 . DOI: 10.16385/j.cnki.issn.1004-4523.202307046
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Year 2025 volume 38 Issue 10
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.202307046
  • Receive Date：2023-07-15
  • Online Date：2026-02-04
Article Data
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History
  • Received：2023-07-15
  • Revised：2023-09-07
Funding
Affiliations
    1.Ningbo Key Laboratory of Advanced Seal Technology, University of NingboTech, Ningbo 315000, China
    2.School of Petrochemical Engineering, Lanzhou University of Technology, Lanzhou 730050, 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
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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