Useful life prediction of woolen hand-knotted carpets using multivariate multiple regression

被引:5
|
作者
Tabatabaei, S. M. [1 ]
Ghane, M. [2 ]
Hamadani, A. Z. [2 ]
Hasani, H. [2 ]
机构
[1] Univ Sci & Arts, Dept Architecture & Art, Yazd, Iran
[2] Isfahan Univ Technol, Dept Text Engn, Esfahan, Iran
关键词
Functional properties; multivariate analysis of variance (MANOVA); multivariate multiple regression; useful life; woolen hand-knotted carpet; IMAGE-ANALYSIS; APPEARANCE; RETENTION; WEAR;
D O I
10.1080/00405000.2016.1193980
中图分类号
TB3 [工程材料学]; TS1 [纺织工业、染整工业];
学科分类号
0805 ; 080502 ; 0821 ;
摘要
In this study, the main purpose is to predict the useful life of woolen hand-knotted carpets using multivariate multiple regression. Thickness loss of surface pile yarns and compression toughness index were chosen as representative of the compression properties. Also, color difference index (Delta E) of pile yarns, tuft size index, and evenness of texture index were considered as representative of the appearance characteristics. Eighteen woolen hand-knotted carpet samples (symmetric knot) with different structural specifications were produced. The carpet samples were subjected to 4000, 8000, and 12000 drum revolutions (wear factor) using a Hexapod tumbler tester and functional properties of samples were investigated in original and worn out carpet samples. At first, the effective variables were selected using multivariate test, and then multivariate analysis of variance was used for evaluating the significance of obtained models. Optimal separate equations of the functional properties on hand-knotted carpets were determined through multivariate multiple regression method. Reverse model of wear factor can be considered as a proper equation to predict the useful life of carpets.
引用
收藏
页码:821 / 828
页数:8
相关论文
共 50 条
  • [31] Online Remaining Useful Lifetime Prediction Using Support Vector Regression
    Martinez, Antonio Leonel Hernandez
    Khursheed, Saqib
    Alnuayri, Turki
    Rossi, Daniele
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 2022, 10 (03) : 1546 - 1557
  • [32] Prediction of Railcar Remaining Useful Life by Multiple Data Source Fusion
    Li, Zhiguo
    He, Qing
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 16 (04) : 2226 - 2235
  • [33] Collision prediction models using multivariate Poisson-lognormal regression
    El-Basyouny, Karim
    Sayed, Tarek
    ACCIDENT ANALYSIS AND PREVENTION, 2009, 41 (04): : 820 - 828
  • [34] Prediction of the chaotic time series using multivariate local polynomial regression
    Zhou Yong-Dao
    Ma Hong
    Lue Wang-Yong
    Wang Hui-Qi
    ACTA PHYSICA SINICA, 2007, 56 (12) : 6809 - 6814
  • [35] Multivariate prediction using softly shrunk reduced-rank regression
    Aldrin, M
    AMERICAN STATISTICIAN, 2000, 54 (01): : 29 - 34
  • [36] LTE QoS Parameters Prediction Using Multivariate Linear Regression Algorithm
    Nasri, Mourad
    Hamdi, Mohamed
    PROCEEDINGS OF THE 2019 22ND CONFERENCE ON INNOVATION IN CLOUDS, INTERNET AND NETWORKS AND WORKSHOPS (ICIN), 2019, : 145 - 150
  • [37] Nonlinear Loads Model for Harmonics Flow Prediction, Using Multivariate Regression
    Lamich, Manuel
    Balcells, Josep
    Corbalan, Montserrat
    Griful, Eulalia
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 64 (06) : 4820 - 4827
  • [39] Prediction of longitudinal dispersion coefficient using multivariate adaptive regression splines
    AMIR HAMZEH HAGHIABI
    Journal of Earth System Science, 2016, 125 : 985 - 995
  • [40] Using multivariate adaptive regression splines (MARS) in pavement roughness prediction
    Attoh-Okine, NO
    Mensah, S
    Nawaiseh, M
    PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT, 2003, 156 (01) : 51 - 55