DETECTION OF THINNING OF HOMOGENEOUS MATERIAL USING ACTIVE THERMOGRAPHY AND CLASSIFICATION TREES

被引:1
作者
Dudzik, Sebastian [1 ]
Dudek, Grzegorz [1 ]
机构
[1] Czestochowa Tech Univ, Fac Elect Engn, Al Armii Krajowej 17, PL-42200 Czestochowa, Poland
关键词
active thermography; classification tree; defect detection and characterization; material thinning detection;
D O I
10.24425/mms.2021.135994
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
Active thermography is an efficient tool for defect detection and characterization as it does not change the properties of tested materials. The detection and characterization process involves heating a sample and then analysing the thermal response. In this paper, a long heating pulse was used on samples with a low thermal diffusivity and artificially created holes of various depths. As a result of the experiments, heating and cooling curves were obtained. These curves, which describe local characteristics of the material, are recognized using a classification tree and divided into categories depending on the material thickness (hole depths). Two advantages of the proposed use of classification trees are: an in-built mechanism for feature selection and a strong reduction in the dimensions of the pattern. Based on the experimental study, it can be concluded that classification trees are a useful tool for the thinning detection of homogeneous material.
引用
收藏
页码:89 / 105
页数:17
相关论文
共 18 条
  • [1] Bishop Christopher M, 2006, PATTERN RECOGN, V128, P1, DOI [10.1117/1.2819119, DOI 10.1117/1]
  • [2] SmcHD1, containing a structural-maintenance-of-chromosomes hinge domain, has a critical role in X inactivation
    Blewitt, Marnie E.
    Gendrel, Anne-Valerie
    Pang, Zhenyi
    Sparrow, Duncan B.
    Whitelaw, Nadia
    Craig, Jeffrey M.
    Apedaile, Anwyn
    Hilton, Douglas J.
    Dunwoodie, Sally L.
    Brockdorff, Neil
    Kay, Graham F.
    Whitelaw, Emma
    [J]. NATURE GENETICS, 2008, 40 (05) : 663 - 669
  • [3] Carslaw H., 1959, Conduction of heat in solids, DOI DOI 10.2307/3610347
  • [4] Recent Advances in Active Infrared Thermography for Non-Destructive Testing of Aerospace Components
    Ciampa, Francesco
    Mahmoodi, Pooya
    Pinto, Fulvio
    Meo, Michele
    [J]. SENSORS, 2018, 18 (02):
  • [5] Segmented infrared image analysis for rotating machinery fault diagnosis
    Duan, Lixiang
    Yao, Mingchao
    Wang, Jinjiang
    Bai, Tangbo
    Zhang, Laibin
    [J]. INFRARED PHYSICS & TECHNOLOGY, 2016, 77 : 267 - 276
  • [6] Classification Tree for Material Defect Detection Using Active Thermography
    Dudek, Grzegorz
    Dudzik, Sebastian
    [J]. INFORMATION SYSTEMS ARCHITECTURE AND TECHNOLOGY, PT I, 2018, 655 : 118 - 127
  • [7] Application of the Naive Bayes Classifier to Defect Characterization Using Active Thermography
    Dudzik, Sebastian
    [J]. JOURNAL OF NONDESTRUCTIVE EVALUATION, 2012, 31 (04) : 383 - 392
  • [8] Dudzik S, 2010, METROL MEAS SYST, V17, P621, DOI 10.2478/v10178-010-0051-3
  • [9] Hastie T., 2009, The Elements of Statistical Learning, DOI [DOI 10.1007/978-0-387-84858-7, 10.1007/978-0-387-84858-7]
  • [10] Ensemble variational Bayes tensor factorization for super resolution of CFRP debond detection
    Lu, Peng
    Gao, Bin
    Feng, Qizhi
    Yang, Yang
    Woo, W. L.
    Tian, Gui Yun
    [J]. INFRARED PHYSICS & TECHNOLOGY, 2017, 85 : 335 - 346