Robust defect detection in ultrasonic nondestructive evaluation (NDE) of difficult materials

In this paper, we present a novel and flexible method for reliable and robust defect detection in difficult materials. It is well known in the literature that the interaction between ultrasonic beams and the insonified medium is a highly nonlinear process, which potentially exhibits distinctive frequency-dependent properties for defects and random reflectors with a degree of randomness. Instead of investigating the structure and pattern of the spectrum of an individual echo, the proposed method focuses on the distinction between the ensembles of defect signals and clutter noise. A training process is used to establish the statistical analysis, based on which a hypothesis test is then applied to received echoes to detect defects. The approach is expected to be adaptive to the material microstructure and characteristics due to the statistical training. Experiments with a 5MHz transducer on austenitic steel samples from a coal fired power station are conducted. Austenitic steel is highly scattering and attenuating, and the method demonstrates accurate and reliable defect detection. When applied to A-scan waveforms, the grain noise is significantly reduced while defect signals are enhanced, and the signal-to-noise ratio (SNR) is improved by about 20dB. As a result, the defect is more visible and can be readily identified in B-scan images. Initial results indicate that this method is robust and delivers good performance without additional calibration and compensation.