RELIABILITY ANALYSIS OF TELESCOPIC ARM OF PIELINE-CATCHING VEHICLE BASED ON SEMI-SUPERVISED DEEP NEURAL NETWORK
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 l...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2025-08-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.08.019 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849227177102409728 |
|---|---|
| author | YUAN Guozhi LIU Wei YAN Zilong ZHANG Ruilin ZHAO Mingxuan SANG Jianbing |
| author_facet | YUAN Guozhi LIU Wei YAN Zilong ZHANG Ruilin ZHAO Mingxuan SANG Jianbing |
| author_sort | YUAN Guozhi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bc21ebe17f10480da850feb6a6f513b4 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2025-08-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-bc21ebe17f10480da850feb6a6f513b42025-08-23T19:00:14ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692025-08-0147159167122984642RELIABILITY ANALYSIS OF TELESCOPIC ARM OF PIELINE-CATCHING VEHICLE BASED ON SEMI-SUPERVISED DEEP NEURAL NETWORKYUAN GuozhiLIU WeiYAN ZilongZHANG RuilinZHAO MingxuanSANG JianbingThe 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.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.08.019Telescopic armReliability analysisSemi-supervised learningDeep neural networksOptimal Latin hypercube sampling |
| spellingShingle | YUAN Guozhi LIU Wei YAN Zilong ZHANG Ruilin ZHAO Mingxuan SANG Jianbing RELIABILITY ANALYSIS OF TELESCOPIC ARM OF PIELINE-CATCHING VEHICLE BASED ON SEMI-SUPERVISED DEEP NEURAL NETWORK Jixie qiangdu Telescopic arm Reliability analysis Semi-supervised learning Deep neural networks Optimal Latin hypercube sampling |
| title | RELIABILITY ANALYSIS OF TELESCOPIC ARM OF PIELINE-CATCHING VEHICLE BASED ON SEMI-SUPERVISED DEEP NEURAL NETWORK |
| title_full | RELIABILITY ANALYSIS OF TELESCOPIC ARM OF PIELINE-CATCHING VEHICLE BASED ON SEMI-SUPERVISED DEEP NEURAL NETWORK |
| title_fullStr | RELIABILITY ANALYSIS OF TELESCOPIC ARM OF PIELINE-CATCHING VEHICLE BASED ON SEMI-SUPERVISED DEEP NEURAL NETWORK |
| title_full_unstemmed | RELIABILITY ANALYSIS OF TELESCOPIC ARM OF PIELINE-CATCHING VEHICLE BASED ON SEMI-SUPERVISED DEEP NEURAL NETWORK |
| title_short | RELIABILITY ANALYSIS OF TELESCOPIC ARM OF PIELINE-CATCHING VEHICLE BASED ON SEMI-SUPERVISED DEEP NEURAL NETWORK |
| title_sort | reliability analysis of telescopic arm of pieline catching vehicle based on semi supervised deep neural network |
| topic | Telescopic arm Reliability analysis Semi-supervised learning Deep neural networks Optimal Latin hypercube sampling |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2025.08.019 |
| work_keys_str_mv | AT yuanguozhi reliabilityanalysisoftelescopicarmofpielinecatchingvehiclebasedonsemisuperviseddeepneuralnetwork AT liuwei reliabilityanalysisoftelescopicarmofpielinecatchingvehiclebasedonsemisuperviseddeepneuralnetwork AT yanzilong reliabilityanalysisoftelescopicarmofpielinecatchingvehiclebasedonsemisuperviseddeepneuralnetwork AT zhangruilin reliabilityanalysisoftelescopicarmofpielinecatchingvehiclebasedonsemisuperviseddeepneuralnetwork AT zhaomingxuan reliabilityanalysisoftelescopicarmofpielinecatchingvehiclebasedonsemisuperviseddeepneuralnetwork AT sangjianbing reliabilityanalysisoftelescopicarmofpielinecatchingvehiclebasedonsemisuperviseddeepneuralnetwork |