Model-Based Iterative Reconstruction for One-Sided Ultrasonic Nondestructive Evaluation

Abstract

One-sided ultrasonic nondestructive evaluation (UNDE) is extensively used to characterize structures that need to be inspected and maintained from defects and flaws that could affect the performance of power plants, such as nuclear power plants. Most UNDE systems send acoustic pulses into the structure of interest, measure the received waveform, and use an algorithm to reconstruct the quantity of interest. The most widely used algorithm in UNDE systems is the synthetic aperture focusing technique (SAFT) because it produces acceptable results in real time. A few regularized inversion techniques with linear models have been proposed which can improve on SAFT, but they tend to make simplifying assumptions that do not address how to obtain reconstructions from large real datasets. In this paper, we propose a model-based iterative reconstruction (MBIR) algorithm designed for scanning UNDE systems. To further reduce some of the artifacts in the results, we enhance the forward model to account for the transmitted beam profile, the occurrence of direct arrival signals, and the correlation between scans from adjacent regions. Next, we combine the forward model with a spatially variant prior model to account for the attenuation of deeper regions. We also present an algorithm to jointly reconstruct measurements from large datasets. Finally, using simulated and extensive experimental data, we show MBIR results and demonstrate how we can improve over SAFT as well as existing regularized inversion techniques.