# Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes
###### Abstract  
At present, researchers in the field of pipeline inspection focus on pipe wall defects while neglecting pipeline defects in special situations such as welds. This poses a threat to the safe operation of projects. In this paper, a multi-node fusion and modal projection algorithm of steel pipes based on guided wave technology is proposed. Through an ANSYS  
However, none of these studies analyzed positioning accuracy. [PERSON] et al. [12] extracted mode L(0,1) and mode L(0,2) from a reflected signal based on the mode shape to identify defects, [PERSON] et al. [13] used superposition technology to enhance the amplitude of modes to detect defects, and [PERSON] et al. [14] evaluated the degree of crack growth based on the energy transmission index of the T(0,1) mode, but none of them determine the location of cracks. [PERSON] et al. [15] expanded the detection range of defects in pipelines through time shift window transformation, which has the problem of frequency domain leakage. [PERSON] et al. [16] used the sensitivity of torsional bending-guided waves to detect axial cracks and quantitatively analyzed the circumferential cracks of pipelines, but they did not analyze radial cracks. [PERSON] et al. [17] detected defects with different radial depths in the welding zone without considering the circumferential length cracks. [PERSON] et al. [18] used the COMSOL Multiphysics software to analyze the axial error of pipelines and obtain the axial positioning error of defects, but they did not evaluate the size of the defects. [PERSON] et al. [19] studied the interaction between the T(0,1) mode and circumferential cracks in welds. They concluded that circumferential cracks with larger circumferential ranges and deeper profiles will produce a great change in the amplitude of the reflected torsional T(0,1) signal, but the relationship between the axial and radial directions and torsional guided waves was not further analyzed. [PERSON] et al. [20] proposed an adjustable Q-factor wavelet transform algorithm to solve the difficulty in extracting the echo characteristics of weld defects, but they failed to locate and evaluate the weld defects. [PERSON] et al. [21] fitted and analyzed weld data using a polynomial model and an expectation maximization algorithm, and they proposed a weld classification method based on the polynomial model, which can calculate weld width and height. [PERSON] et al. [22] adjusted the AlexNet model according to the characteristics of weld defects and adopted a deep learning algorithm. The detection success rate of AlexNet is as high as 97%, and it improves the accuracy of pipeline weld defect detection, but increasing the depth and width of the model leads to increases in calculation and model training times.  
In this article, the semi-analytical finite element method is used to study the propagation law of guided waves in a pipeline. By establishing the T(0,1) mode and L(0,2) mode, the UGW detection method for pipeline axial defects and weld defects is studied using numerical simulation. Wavelet transform and Hilbert transform are used to locate the pipeline cracks accurately and analyze the error level accurately. The algorithm of multi-node fusion and modal projection is adopted for defects in pipeline welds, and the influence law of the defect reflection coefficient with the change in the circumferential and radial dimensions of defects is obtained through simulation.