Compressive sensing of ultrasonic array data with full matrix capture in nozzle welds inspection

The phased array ultrasonic technique (PAUT) with full matrix capture (FMC) exhibits the advantages of high imaging accuracy and great defect characterization ability, which play important roles in the nondestructive testing of welded structures. To address the problem of a large amount of signal acquisition, storage, and transmission data in nozzle weld defect monitoring, a PAUT with an FMC data compression method based on compressive sensing (CS) was proposed. To accomplish this, the detection of nozzle welds using PAUT with FMC was performed by simulation and experiment, and the obtained FMC data were compressed and reconstructed. A suitable sparse representation was found dedicated to the FMC data of nozzle welds, and the reconstruction performance was compared between the greedy theory-based orthogonal matching pursuit (OMP) algorithm and the convex optimization theory-based basis pursuit (BP). Also, an empirical mode decomposition (EMD)-based intrinsic mode function (IMF) circular matrix was constructed to provide another idea for the construction of the sensing matrix. Although the experimental results were not able to reach the ideal effect in the simulation, the image was restored accurately with a small number of measured values, and flaw identification could be guaranteed, indicating that the CS algorithm can effectively improve the defect detection efficiency of the phased array.

Automated summary

The Phased Array Ultrasonic Technique (PAUT) with Full Matrix Capture (FMC) is advantageous for high imaging accuracy and defect characterization in nondestructive testing of welded structures. In order to address the large amount of data in nozzle weld defect monitoring, a PAUT with FMC data compression method based on compressive sensing (CS) was proposed. The study compared the reconstruction performance of the greedy theory-based orthogonal matching pursuit (OMP) algorithm and the convex optimization theory-based basis pursuit (BP), and also explored an alternative sensing matrix construction using empirical mode decomposition (EMD)-based intrinsic mode function (IMF) circular matrix.