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

Abstract

The 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 which enables 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 either side of the inspected region, and due to the cylindrical nature of the pipe, waves are able to travel in an inifinite 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. Index Termstomography, guided wave, Lamb wave, signal separation, helical paths Manuscript received XXXXXXX X, XXXX.All authors are with the

Guided waves for damage identification in pipeline structures: A review

“…It is noticed that both MUT and HUT scanning schemes only consider a single mode of interest under a predetermined frequency, such that incomplete datasets sometimes reduce the accuracy of the image reconstruction. Certain methods have thus been developed to improve these techniques, such as frequency compounding and helical path separation …”

“…Certain methods have thus been developed to improve these techniques, such as frequency compounding [71] and helical path separation. [72] Another algorithm used with the concept of tomography is a reconstruction algorithm for probabilistic inspection of damage (RAPID). Fundamentally, this algorithm extracts a signal difference coefficient from the current signal and the baseline signal of the structure, accounting for changes in signal amplitude, wave velocity and mode conversion, [73] and then forms ray ellipses around several pairs of source receivers, which will superpose and generate the final tomogram.…”

Inspection and monitoring systems subsea pipelines: A review paper

“…118,119 In addition to the hardware-based challenges of GWT, the presence of dispersion, scattering, and multiple modes can make data interpretation a difficult task. There is ongoing research in classification 120 and visualization/tomography [121][122][123] of defects based on GWT data. Figure 18 shows an example of GWT-based tomography to visualize defects in a pipe bend.…”

Experimental Studies of the Inspection of Areas With Restricted Access Using A0 Lamb Wave Tomography

“…Inversely, separating the various wave packets corresponding to the respective helical order increases the length of the two linear arrays (considering the unwrapped pipe) and the range of missing view angles decreases, leading to a less ill-posed reconstruction problem. This is achieved by employing the iterative algorithm proposed in [24], which makes use of the known location of the transducers and subsequently the associated propagation distances of the different wave packets, as well as the expected phase shifts between adjacent transducers to separate out the various wave packets. The algorithm has been successfully tested on numerical and experimental data, also in the presence of a defect, and the interested reader is pointed towards [24].…”

“…The measured time traces are first corrected for dispersion, using the algorithm presented in [28], where the amplitude-time traces are remapped to amplitude-distance traces, while removing the effects of dispersion to improve signal interpretation and reduce signal overlap (this is more helpful with strongly dispersive modes). The next processing step involves separating out the various helical orders that are to be used for the thickness reconstruction by employing the algorithm from [24] and a total of seven orders are extracted from the original dataset.…”