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Architectural design of fault diagnosis methods for aircraft complex motion mechanisms
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Dongyu HE1, Yin YIN1, Taotao LIANG2, Aojie DONG1, Peng ZHANG1, Xiaohui WEI1, Hong NIE1
Journal of Vibration Engineering | 2025, 38(6) : 1167 - 1182
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Journal of Vibration Engineering | 2025, 38(6): 1167-1182
Architectural design of fault diagnosis methods for aircraft complex motion mechanisms
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Dongyu HE1, Yin YIN1, Taotao LIANG2, Aojie DONG1, Peng ZHANG1, Xiaohui WEI1, Hong NIE1
Affiliations
  • 1.College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • 2.College of General Aviation and Flight,Nanjing University of Aeronautics and Astronautics,Liyang 213300,China
Published: 2025-06-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.06.005
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Current research on fault diagnosis for aircraft complex motion mechanisms primarily focuses on system functional failure analysis, neglecting a comprehensive understanding of the correlation between motion characteristics and actual faults. This study investigates fault diagnosis methods for complex motion mechanisms and proposes a three-tiered framework encompassing data generation, feature processing and data analysis to address this limitation. The framework utilizes dynamic modeling and a fault parameter system to generate a dataset of time-series signals representing typical fault conditions. One-dimensional time-series data are mapped using two-dimensional image conversion methods, constructing multidimensional tensors through feature-level fusion based on sensor types and feature extraction methods of the complex motion mechanisms. A deep learning-based fault diagnosis model is employed for precise fault identification of complex motion mechanisms. This framework further incorporates collaborative feature transformations using Gramian angular fields and Markov transition fields, as well as residual network models with channel and spatial attention mechanisms. Experimental validation using a landing gear lower strut lock mechanism demonstrates high accuracy, exceeding 0.9566 at a 95% confidence level, thus validating the feasibility of this approach for fault diagnosis in aircraft complex motion mechanisms. Ablation experiments confirm the effectiveness of each component, highlighting the overall superiority of the proposed framework.

fault diagnosis  /  architecture  /  feature fusion  /  residual network  /  attention mechanism  /  lower lever lock
Dongyu HE, Yin YIN, Taotao LIANG, Aojie DONG, Peng ZHANG, Xiaohui WEI, Hong NIE. Architectural design of fault diagnosis methods for aircraft complex motion mechanisms[J]. Journal of Vibration Engineering, 2025 , 38 (6) : 1167 -1182 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.06.005
Year 2025 volume 38 Issue 6
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.06.005
  • Receive Date:2025-04-30
  • Online Date:2026-02-12
  • Published:2025-06-10
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  • Received:2025-04-30
  • Revised:2025-05-22
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Affiliations
    1.College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
    2.College of General Aviation and Flight,Nanjing University of Aeronautics and Astronautics,Liyang 213300,China
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表12种不同金属材料的力学参数

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Number 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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