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Reliability analysis of telescopic arm of pieline-catching vehicle based on semi-supervised deep neural network
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Guozhi YUAN1, Wei LIU1, Zilong YAN2, Ruilin ZHANG1, Mingxuan ZHAO1, Jianbing SANG1
Journal of Mechanical Strength | 2025, 47(8) : 159 - 167
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Journal of Mechanical Strength | 2025, 47(8): 159-167
Optimization·Reliability
Reliability analysis of telescopic arm of pieline-catching vehicle based on semi-supervised deep neural network
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Guozhi YUAN1, Wei LIU1, Zilong YAN2, Ruilin ZHANG1, Mingxuan ZHAO1, Jianbing SANG1
Affiliations
  • 1.School of Mechanical Engineering, Hebei University of Technology, Tianjin 300400, China
  • 2.Langfang Jinglong Heavy Equipment Co., Ltd., Langfang 065300, China
Published: 2025-08-15 doi: 10.16579/j.issn.1001.9669.2025.08.019
Outline
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The telescopic arm, a pivotal component in the pipeline grabbing vehicle, links the lifting platform and the mechanical claw, shouldering the majority of the load. Conducting a reliability analysis is imperative. Traditional methods for reliability face challenges like high computational costs and low accuracy dealing with multidimensional uncertainties. To overcome these, our study proposed an engineering mechanical reliability analysis method, leveraging Adams dynamic simulation, semi-supervised learning, deep neural networks, and Monte Carlo method. In this study, a virtual prototype model of the pipeline grabbing vehicle was established, identifying hazardous operating conditions. Combining the telescopic arm model’s geometric parameters and overall structure, uncertain factors influencing the maximum von Mises stress were determined, conducting a sensitivity analysis was conducted. Utilizing optimal Latin hypercube sampling based on uncertain parameter distributions, Ansys Workbench was employed to build a finite element model, obtain output results for the sample size. Semi-supervised learning processed the finite element simulation data, enhanced deep neural network training accuracy.Finally, based on the fourth strength theory, a failure criteria for the telescopic arm component was determined. Combining deep neural networks and Monte Carlo method, the reliability and failure probability were predicted. Results show that this method surpasses actual engineering precision requirements,provides a certain guiding significance.

Telescopic arm  /  Reliability analysis  /  Semi-supervised learning  /  Deep neural networks  /  Optimal Latin hypercube sampling
Guozhi YUAN, Wei LIU, Zilong YAN, Ruilin ZHANG, Mingxuan ZHAO, Jianbing SANG. Reliability analysis of telescopic arm of pieline-catching vehicle based on semi-supervised deep neural network[J]. Journal of Mechanical Strength, 2025 , 47 (8) : 159 -167 . DOI: 10.16579/j.issn.1001.9669.2025.08.019
  • Natural Science Foundation of Hebei Province(A2020202015)
  • National Defense Science and Technology Key Laboratory Fundation
Year 2025 volume 47 Issue 8
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Article Info
doi: 10.16579/j.issn.1001.9669.2025.08.019
  • Receive Date:2023-10-13
  • Online Date:2026-03-19
  • Published:2025-08-15
Article Data
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History
  • Received:2023-10-13
  • Revised:2024-03-08
Funding
Natural Science Foundation of Hebei Province(A2020202015)
National Defense Science and Technology Key Laboratory Fundation
Affiliations
    1.School of Mechanical Engineering, Hebei University of Technology, Tianjin 300400, China
    2.Langfang Jinglong Heavy Equipment Co., Ltd., Langfang 065300, China

Corresponding:

SANG Jianbing, E-mail:
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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