ISSN   1004-0595

CN  62-1224/O4

Advanced Search
Forecasting of the Wear Trend of Tribosystems Using Artificial Neural Networks[J]. TRIBOLOGY, 1996, 16(3): 267-271.
Citation: Forecasting of the Wear Trend of Tribosystems Using Artificial Neural Networks[J]. TRIBOLOGY, 1996, 16(3): 267-271.
shu

Forecasting of the Wear Trend of Tribosystems Using Artificial Neural Networks

More Information
    Graphical Abstract
    • Abstract
  • For the parallel distributed processing,associative memory,self organization,self learning and strong mapping abilities,artificial neural networks have shown broad application prospects in many fields. The method and procedure of single variable time series forecasting based on neural networks are described.The forecasting model for the wear trend of tribosystems is proposed,in which the quantitative ferrographic parameter Q is used as the characteristic parameter for forecasting wear trend.The single step and multi step forecasting method for the wear trend forecasting based on the neural network are discussed.The model is applied to the wear trend of the gear pump.The results are very satisfactory.
    • FullText(HTML)
    • References (0)
    • Related Articles

  • Related Articles

    [1]ZHANG Yongfang, HOU Pihong, YANG Xinliang, CHEN Ruibo, KANG Jianxiong, XING Zhiguo, LÜ Yanjun. A Prediction Method of Surface Wear in Internal Combustion Engine Cylinder Liners Using Multi-Scale Combined Recurrent Neural Networks[J]. TRIBOLOGY, 2025, 45(2): 315-324. DOI: 10.16078/j.tribology.2024002
    [2]SHI Xinfa, HE Shizhong, XIE Xiaopeng, SUN Yuhang. Review on Feature Extraction of Lubrication and Wear Fault Diagnosis in Tribology System[J]. TRIBOLOGY, 2023, 43(3): 241-255. DOI: 10.16078/j.tribology.2021066
    [3]GUAN Haojian, HE Shizhong, LI Qiuqiu, YANG Zhihong, QIN Chudong, HE Weichu. A Review of Convolutional Neural Networks in Equipment Wear Particle Recognition[J]. TRIBOLOGY, 2022, 42(2): 426-445. DOI: 10.16078/j.tribology.2021025
    [4]Modeling of Wear Process of V9-Cr4-Mo3 High Speed Steel Cooling Roll Via BP Neural Network[J]. TRIBOLOGY, 2006, 26(6): 541-545.
    [5]Prediction Modeling of Friction Behavior of Aluminum Matrix Composites Using Neural Network and Genetic Algorithms under High Velocity and Dry Sliding Condition[J]. TRIBOLOGY, 2005, 25(6): 545-549.
    [6]Diagnosis of Wear-Induced Breakdown of Machine by Spectrometric Analysis Based on Artificial Neural Network[J]. TRIBOLOGY, 2004, 24(3): 263-267.
    [7]Neural-Network-Based Fusion Diagnosis Using Various Oil Analysis Techniques[J]. TRIBOLOGY, 2003, 23(5): 431-434.
    [8]Wear Debris Identification Based on Rough Set-Artificial Neural Networks[J]. TRIBOLOGY, 2002, 22(3): 235-237.
    [9]Neural Network Model for Examination of the Temperature Characteristics of Boundary Film on Friction Surface[J]. TRIBOLOGY, 2000, 20(6): 469-471.
    [10]Tribological Design of Diamond Tools for Stone Processing Using Artificial Neural Network (ANN) Computer Technique[J]. TRIBOLOGY, 1997, 17(2): 171-177.

Catalog

    Get Citation
    PDF
    XML
    Article views (1550) PDF downloads (1617) Cited by()
    Turn off MathJax
    Article Contents
    Abstract
    Related Articles

    Author Center

    Email Alert RSS

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return