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A deep neural network method for rapid localization of aircraft abnormal dynamic loads
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Shu-ya LIANG1, 3, Xin-wei XU2, Te YANG1, 3, Le WANG1, 3, Zhi-chun YANG1, 3
Journal of Vibration Engineering | 2024, 37(10) : 1651 - 1659
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Journal of Vibration Engineering | 2024, 37(10): 1651-1659
A deep neural network method for rapid localization of aircraft abnormal dynamic loads
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Shu-ya LIANG1, 3, Xin-wei XU2, Te YANG1, 3, Le WANG1, 3, Zhi-chun YANG1, 3
Affiliations
  • 1School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
  • 2Sichuan Institute of Aerospace System Engineering, Chengdu 610100, China
  • 3National Key Laboratory of Strength and Structural Integrity, Xi’an 710072, China
Published: 2024-10-28 doi: 10.16385/j.cnki.issn.1004-4523.2024.10.002
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Aircraft often operate in complex and variable dynamic load environment，and dynamic load localization is the primary problem that needs to be solved in this field. This paper focuses on the dynamic load localization requirements of common and prone to abnormal vibrations in aircraft structures. Combining deep neural network，a rapid dynamic load localization method for aircraft structures is developed. By using Long Short-Term Memory （LSTM） neural network，the inverse implicit function model which can accurately describe the corresponding relationship between the dynamic load location and vibration response of the structure is constructed. A dynamic load localization method based on the LSTM neural network classification model is proposed. A simplified finite element model of the entire aircraft structure is established to simulate several typical dynamic load conditions that the aircraft may encounter during actual flight. The noise resistance and robustness of the established deep neural network are also studied. The simulation results show that the proposed method can accurately identify the location of dynamic loads under various load conditions，and can still maintain high locating accuracy under the measurement noise level of 10 dB and the parameter perturbation of 2.8%.

dynamic load localization  /  deep neural network  /  LSTM neural network  /  aircraft structure  /  inverse problem
Shu-ya LIANG, Xin-wei XU, Te YANG, Le WANG, Zhi-chun YANG. A deep neural network method for rapid localization of aircraft abnormal dynamic loads[J]. Journal of Vibration Engineering, 2024 , 37 (10) : 1651 -1659 . DOI: 10.16385/j.cnki.issn.1004-4523.2024.10.002
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2024.10.002
  • Receive Date：2024-02-07
  • Online Date：2026-02-12
  • Published：2024-10-28
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History
  • Received：2024-02-07
  • Revised：2024-04-13
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Affiliations
    1School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
    2Sichuan Institute of Aerospace System Engineering, Chengdu 610100, China
    3National Key Laboratory of Strength and Structural Integrity, Xi’an 710072, China
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
species		 占总种数比例
Percentage of
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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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