An analytical approach to reconstruction of axisymmetric defects in pipelines using T(0,1)guided waves

Torsional guided waves have been widely utilized to inspect the surface corrosion in pipelines due to their simple displacement behaviors and the ability of longrange transmission.Especially,the torsional mode T(0,1),which is the first order of torsional guided waves,plays the irreplaceable position and role,mainly because of its non-dispersion characteristic property.However,one of the most pressing challenges faced in modern quality inspection is to detect the surface defects in pipelines with a high level of accuracy.Taking into account this situation,a quantitative reconstruction method using the torsional guided wave T(0,1)is proposed in this paper.The methodology for defect reconstruction consists of three steps.First,the reflection coefficients of the guided wave T(0,1)scattered by different sizes of axisymmetric defects are calculated using the developed hybrid finite element method(HFEM).Then,applying the boundary integral equation(BIE)and Born approximation,the Fourier transform of the surface defect profile can be analytically derived as the correlative product of reflection coefficients of the torsional guided wave T(0,1)and the fundamental solution of the intact pipeline in the frequency domain.Finally,reconstruction of defects is precisely performed by the inverse Fourier transform of the product in the frequency domain.Numerical experiments show that the proposed approach is suitable for the detection of surface defects with arbitrary shapes.Meanwhile,the effects of the depth and width of surface defects on the accuracy of defect reconstruction are investigated.It is noted that the reconstructive error is less than 10%,providing that the defect depth is no more than one half of the pipe thickness.

A novel physics-informed framework for reconstruction of structural defects

The ultrasonic guided wave technology plays a significant role in the field of non-destructive testing as it employs acoustic waves with the advantages of high propagation efficiency and low energy consumption during the inspect process.However,the theoretical solutions to guided wave scattering problems with assumptions such as the Born approximation have led to the poor quality of the reconstructed results.Besides,the scattering signals collected from industry sectors are often noised and nonstationary.To address these issues,a novel physics-informed framework(PIF)for the quantitative reconstruction of defects by means of the integration of the data-driven method with the guided wave scattering analysis is proposed in this paper.Based on the geometrical information of defects and initial results obtained by the PIF-based analysis of defect reconstructions,a deep-learning neural network model is built to reveal the physical relationship between the defects and the noisy detection signals.This learning model is then adopted to assess and characterize the defect profiles in structures,improve the accuracy of the analytical model,and eliminate the impact of the noise pollution in the process of inspection.To demonstrate the advantages of the developed PIF for the complex defect reconstructions with the capability of denoising,several numerical examples are carried out.The results show that the PIF has greater accuracy for the reconstruction of defects in the structures than the analytical method,and provides a valuable insight into the development of artificial intelligence(AI)-assisted inspection systems with high accuracy and efficiency in the fields of structural integrity and condition monitoring.

The Multi-field Coupled Vibration Analysis of AT-Cut Quartz Crystal Resonators with Parallelism Error

During the fabrication of quartz crystal resonators(QCRs),parallelism error is inevitably generated,which is rarely investigated.In order to reveal the influence of parallelism error on the working performance of QCRs,the coupled vibration of a non-parallel AT-cut quartz crystal plate with electrodes is systematically studied from the views of theoretical analysis and numerical simulations.The two-dimensional thermal incremental field equations are solved for the free vibration analysis via the coefficient-formed partial differential equation module of the COMSOL Multiphysics software,from which the frequency spectra,frequency–temperature curves,and mode shapes are discussed in detail.Additionally,the piezoelectric module is utilized to obtain the admittance response under different conditions.It is demonstrated that the parallelism error reduces the resonant frequency.Additionally,symmetry broken by the non-parallelism increases the probability of activity dip and is harmful to QCR’s thermal stability.However,if the top and bottom surfaces incline synchronously in the same direction,the influence of parallelism error is tiny.The conclusions achieved are helpful for the QCR design,and the methodology presented can also be applied to other wave devices.

一种基于深度学习的超声导波缺陷重构方法

超声导波检测因其传播效率高、耗能少等优势成为了无损检测领域的重要研究方向.目前已有的利用超声导波进行结构缺陷探测和定量化重构的方法主要由相关的导波散射理论推导得出.然而,由于导波散射问题本身的高复杂性,使得在推导上述理论方法时引入一些近似假设,降低了重构结果的质量.另外,有些方法通过优化迭代的方式提高重构精度,又会增加检测的时间成本.有鉴于此,论文探索了一种将卷积神经网络与导波散射理论模型以局部融合的方式实现缺陷定量化重构的新方法.应用样本数据训练后的神经网络实现缺陷定量化重构,弥补缺陷重构过程中的理论模型误差,同时去除在实际检测过程中所存在的环境噪声.论文以利用SH导波重构平板中的减薄缺陷为研究对象,通过数值模拟验证了该方法在缺陷重构时具有高效率和高精度的特点,特别是对矩形缺陷的重构,新方法的结果精度比波数空间域变换法的精度提高了近200%.

A More Accurate Reconstruction Method for Detecting Large-Depth Defects in Plates Using SH Guided Waves

The inspection of thickness thinning defects and corrosion defects is greatly significant for the health prediction of plate structures.The main aim of this research is to propose a novel and effective approach to achieve the accurate and rapid detection of arbitrary defects using shear horizontal(SH)guided waves,particularly for large-depth and complex defects.The proposed approach combines the quantitative detection of Fourier transform with a reference model-based strategy to improve the accuracy of large-depth defect detection.Since the shallow defect profile is theoretically constructed by inverse Fourier transform of the product of reflection coefficients and integral coefficients of reference models,the unknown large-depth defect can be initially assessed using the relevant information from a predefined reference model.By iteratively updating the integral coefficients of reference models,the accuracy of reconstruction of large-depth defects is much improved.To achieve the converged defect profile,a termination criterion,the root mean square error,is applied to guarantee the construction of defects with a high level of accuracy.Moreover,the hybrid finite element method is used to simulate the propagation of SH guided waves in plates for calculating the reflection coefficients of plates with defects.Finally,to demonstrate the capability of the developed reconstruction method for defect detection in terms of accuracy and efficiency,three types of large-depth defect profiles,i.e.,a rectangular flaw,a double-rectangular flaw and a complex flaw,are examined.Results show that the discrepancy between the predicted defect profile and the real one is quite small,even in the largest-depth defect case where the defect depth is equal to 0.733 times the plate thickness,the minimal difference is observed.It is noted that the fast convergence of the proposed approach can be achieved by no more than ten updates for the worst case.