Literature DB >> 25965685

Robust helical path separation for thickness mapping of pipes by guided wave tomography.

Peter Huthwaite, Matthias Seher.   

Abstract

Pipe wall loss caused by corrosion can be quantified across an area by transmitting guided Lamb waves through the region and measuring the resulting signals. Typically the dispersive relationship for these waves, which means that wave velocity is a known function of thickness, is exploited, enabling the wall thickness to be determined from a velocity reconstruction. The accuracy and quality of this reconstruction is commonly limited by the angle of view available from the transducer arrays. These arrays are often attached as a pair of ring arrays on either side of the inspected region, and due to the cylindrical nature of the pipe, waves are able to travel in an infinite number of helical paths between any two transducers. The first arrivals can be separated relatively easily by time gating, but by using just these components the angle of view is severely restricted. To improve the viewing angle, it is necessary to separate the wavepackets. This paper provides an outline of a separation approach: initially the waves are backpropagated to their source to align the different signals, then a filtering technique is applied to select the desired components. The technique is applied to experimental data and demonstrated to robustly separate the signals.

Mesh:

Year:  2015        PMID: 25965685     DOI: 10.1109/TUFFC.2014.006884

Source DB:  PubMed          Journal:  IEEE Trans Ultrason Ferroelectr Freq Control        ISSN: 0885-3010            Impact factor:   2.725


  2 in total

1.  Measurement of Pipe and Liquid Parameters Using the Beam Steering Capabilities of Array-Based Clamp-On Ultrasonic Flow Meters.

Authors:  Jack Massaad; Paul L M J van Neer; Douwe M van Willigen; Michiel A P Pertijs; Nicolaas de Jong; Martin D Verweij
Journal:  Sensors (Basel)       Date:  2022-07-06       Impact factor: 3.847

2.  Elbow Damage Identification Technique Based on Sparse Inversion Image Reconstruction.

Authors:  Yu Wang; Xueyi Li
Journal:  Materials (Basel)       Date:  2020-04-10       Impact factor: 3.623

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.