Quantification and localization of internal pipe damage

被引：15
|
作者：
Vogelaar, Bouko [1 ]
Golombok, Michael [2 ]
机构：
[1] Eindhoven Univ Technol, Dept Mech Engn, Groene Loper 15, NL-5600 MB Eindhoven, Netherlands
[2] Shell Global Solut Int BV, Kessler Pk 1, NL-2288 GS Rijswijk, Netherlands
来源：
MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2016年 / 78卷
关键词：
Structural health monitoring; Guided waves; Piezoelectric transducers; Pipe damage; Time-frequency analysis; FUNDAMENTAL TORSIONAL MODE; LAMB WAVES; GUIDED-WAVE; CHIRP EXCITATION; DEFECT DETECTION; BASE-LINE; IDENTIFICATION; PROPAGATION; SCATTERING; ARRAY;
D O I：
10.1016/j.ymssp.2015.10.011
中图分类号：
TH [机械、仪表工业];
学科分类号：
0802 ;
摘要：
Internal pipeline defects are detectable and locatable from guided acoustic wave reflections using sensors mounted on the outer wall of a pipe. We demonstrate pipeline integrity monitoring with only two single acoustic sensors. Multi-mode dispersion imaging of shear displacement shows that the pure torsional mode is the only wave that survives axisymmetric pipe reflection. A reduction of 20% of the reference reflection is measured for a damage of half the wall thickness. This natural filtering is used to quantify and locate internal pipe damage. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码：107 / 117
页数：11
相关论文
共 50 条
  • [21] Uncertainty quantification of damage localization based on a probabilistic convolutional neural network
    Lu, H. Y.
    Mardanshahi, A.
    Cantero-Chinchilla, S.
    Gryllias, K.
    Chronopoulos, D.
    XII INTERNATIONAL CONFERENCE ON STRUCTURAL DYNAMICS, EURODYN 2023, 2024, 2647
  • [22] Experimental study on localization and quantification of structural damage using ZigBee Motes
    Qian, Y.
    Mita, A.
    Clayton, E. H.
    Dyke, S. J.
    PROCEEDINGS OF THE THIRD EUROPEAN WORKSHOP STRUCTURAL HEALTH MONITORING 2006, 2006, : 405 - +
  • [23] Experimental Damage Localization and Quantification with a Numerically Trained Convolutional Neural Network
    Postorino, Hadrien
    Monteiro, Eric
    Rebillat, Marc
    Mechbal, Nazih
    EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 3, 2023, : 401 - 407
  • [24] Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks
    Monteiro, Daniele Kauctz
    Miguel, Leticia Fleck Fadel
    Zeni, Gustavo
    Becker, Tiago
    de Andrade, Giovanni Souza
    de Barros, Rodrigo Rodrigues
    SHOCK AND VIBRATION, 2023, 2023
  • [25] On-line structural damage localization and quantification using wireless sensors
    Hsu, Ting-Yu
    Huang, Shieh-Kung
    Lu, Kung-Chung
    Loh, Chin-Hsiung
    Wang, Yang
    Lynch, Jerome Peter
    SMART MATERIALS AND STRUCTURES, 2011, 20 (10)
  • [26] A local flexibility method for vibration-based damage localization and quantification
    Reynders, Edwin
    De Roeck, Guido
    JOURNAL OF SOUND AND VIBRATION, 2010, 329 (12) : 2367 - 2383
  • [27] Numerical prediction of the ductile damage for axial cracks in pipe under internal pressure
    Ismahene, Fezazi Amina
    Belaid, Mechab
    Mokadem, Salem
    Boualem, Serier
    FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY, 2021, (58): : 231 - 241
  • [28] Effect of internal surface damage on vibration behavior of a composite pipe conveying fluid
    Oke, Wasiu A.
    Khulief, Yehia A.
    COMPOSITE STRUCTURES, 2018, 194 : 104 - 118
  • [29] Numerical prediction of the ductile damage for axial cracks in pipe under internal pressure
    Ismahène, Fezazi Amina
    Belaïd, Mechab
    Mokadem, Salem
    Boualem, Serier
    Frattura ed Integrita Strutturale, 2021, 15 (58): : 231 - 241
  • [30] Deformation localization and internal residual stresses in billets for Zr-Nb pipe rolling
    Zuev, LB
    Semukhin, BS
    Zavodchikov, SY
    MATERIALS LETTERS, 2002, 57 (04) : 1015 - 1020
12345