Loader Boom Reliability Analysis with Artificial Neural Network Method

被引:1
|
作者
Sha, Lirong [1 ]
Yang, Yue [1 ]
机构
[1] Jilin Jianzhu Univ, Changchun 130118, Peoples R China
关键词
Reliability; Artificial neural network; Fatigue; Response method;
D O I
10.4028/www.scientific.net/AMR.838-841.250
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The artificial neural network is used to solve the reliability analysis of the engineering structure. When the limit state function of structure is highly complex or with nonlinearity, it is time-consuming or cumbersome to carry out reliability analysis with traditional methods. The artificial neural network response surface method is adopted to analyze the fatigue reliability of loader boom, the working process of loader machine is analyzed with FEM software and analytical method, the stress-time history and strain-tithe history of loader boom are schematized with rain-flow algorithm, consequently the fatigue life analysis on the structure can be carried out with local stress-strain method. The artificial neural network method is used to fit the performance function as well as its derivatives, so as to calculate the reliability of the structure. The numerical example results show that the proposed method has capability of solving industrial-scale reliability problems.
引用
收藏
页码:250 / 253
页数:4
相关论文
共 50 条
  • [11] Method in analysis of CdZnTe γ spectrum with artificial neural network
    Ai, Xian-Yun
    Wei, Yi-Xiang
    Xiao, Wu-Yun
    Hedianzixue Yu Tance Jishu/Nuclear Electronics and Detection Technology, 2005, 25 (06): : 626 - 629
  • [12] Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network
    Shafiq, Anum
    Colak, Andac Batur
    Swarup, Chetan
    Sindhu, Tabassum Naz
    Lone, Showkat Ahmad
    ADVANCED THEORY AND SIMULATIONS, 2022, 5 (08)
  • [13] Artificial neural network analysis for reliability prediction of regional runoff utilization
    S. C. Lee
    H. T. Lin
    T. Y. Yang
    Environmental Monitoring and Assessment, 2010, 161 : 315 - 326
  • [14] Artificial neural network analysis for reliability prediction of regional runoff utilization
    Lee, S. C.
    Lin, H. T.
    Yang, T. Y.
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2010, 161 (1-4) : 315 - 326
  • [15] Fault tolerant design and reliability evaluation method for artificial neural network controller
    Zhang, Tao
    Hu, Dongcheng
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 1999, 27 (02): : 4 - 7
  • [16] Artificial neural network for software reliability assessment
    Adnan, WA
    Yaakob, M
    Anas, R
    Tamjis, MR
    IEEE 2000 TENCON PROCEEDINGS, VOLS I-III: INTELLIGENT SYSTEMS AND TECHNOLOGIES FOR THE NEW MILLENNIUM, 2000, : B446 - B451
  • [17] The hybrid uncertain neural network method for mechanical reliability analysis
    Peng, Wensheng
    Zhang, Jianguo
    You, Lingfei
    INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES, 2015, 16 (04) : 510 - 519
  • [18] Pulse transiently chaotic neural network for the analysis method of reliability
    Zhang, J. (zjg@buaa.edu.cn), 1600, Chinese Society of Astronautics (35):
  • [19] Small-sample artificial neural network based response surface method for reliability analysis of concrete bridges
    Lehky, D.
    Somodikova, M.
    LIFE-CYCLE OF STRUCTURAL SYSTEMS: DESIGN, ASSESSMENT, MAINTENANCE AND MANAGEMENT, 2015, : 1903 - 1909
  • [20] Finite Element Analysis of the Loader Boom Based on ABAQUS
    He, Yu-jing
    Tang, Ya-dong
    Shi, Jing-zhao
    Li, He
    PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON MANUFACTURING ENGINEERING AND INTELLIGENT MATERIALS (ICMEIM 2017), 2017, 100 : 54 - 59