No abstract

Ultrasonic detection and characterization of flaws in coarse-grained materials exhibiting heterogeneous and scattering microstructure is of particular importance across many industries, but remains challenging. Most spectral based denoising methods in the literature are sensitive to material properties, which necessitate a troublesome parameter optimization process and consequently impede their application into ultrasonic image processing. In order to improve flaw visibility in an image, we propose a novel and robust clutter suppression method through spectral distribution similarity analysis (SDSA). This method isometrically segments all the time-series data in a dataset acquired by the Full-Matrix-Capture technique and then censuses the spectral distribution of global segments and of local segments for every focusing point in the Total-FocusingMethod image. The coefficient computed by measuring the similarity between the two spectral distributions reveals the possibility of a legitimate flaw indication. Experiments on two highly scattering samples were conducted to validate this method. By applying SDSA, crack visibility is greatly enhanced with an average >20 dB target-to-noise ratio enhancement for a stainless steel weld sample, whilst ~30dB improvement for an austenitic steel sample. The proposed technique retains excellent performance for both samples when the selected segment length is varied, proving its robustness and highlighting its potential for application across various 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

This paper presents a robust frequency diversity based algorithm for clutter reduction in ultrasonic A-scan waveforms. The performance of conventional spectral-temporal techniques like Split Spectrum Processing (SSP) is highly dependent on the parameter selection, especially when the signal to noise ratio (SNR) is low. Although spatial beamforming offers noise reduction with less sensitivity to parameter variation, phased array techniques are not always available. The proposed algorithm first selects an ascending series of frequency bands. A signal is reconstructed for each selected band in which a defect is present when all frequency components are in uniform sign. Combining all reconstructed signals through averaging gives a probability profile of potential defect position. To facilitate data collection and validate the proposed algorithm, Full Matrix Capture is applied on the austenitic steel and high nickel alloy (HNA) samples with 5MHz transducer arrays. When processing A-scan signals with unrefined parameters, the proposed algorithm enhances SNR by 20dB for both samples and consequently, defects are more visible in B-scan images created from the large amount of A-scan traces. Importantly, the proposed algorithm is considered robust, while SSP is shown to fail on the austenitic steel data and achieves less SNR enhancement on the HNA data

A number of materials used in industry exhibit highly-scattering properties which can reduce the performance of conventional ultrasonic NDE approaches. Moving Bandwidth Polarity Thresholding (MBPT) is a robust frequency diversity based algorithm for scatter noise reduction in single A-scan waveforms, using sign coherence across a range of frequency bands to reduce grain noise and improve Signal to Noise Ratio. Importantly, for this approach to be extended to array applications, spatial variation of noise characteristics must also be considered. This paper presents a new spatial-frequency diversity based algorithm for array imaging, extended from MBPT. Each A-scan in the full matrix capture array dataset is partitioned into a serial of overlapped frequency bands and then undergoes polarity thresholding to generate sign-only coefficients indicating possible flaw locations within each selected band. These coefficients are synthesized to form a coefficient matrix using a delay and sum approach in each frequency band. Matrices produced across the frequency bands are then summed to generate a weighting matrix, which can be applied on any conventional image. A 5MHz linear array has been used to acquire data from both austenitic steel and high nickel alloy (HNA) samples to validate the proposed algorithm. Background noise is significantly suppressed for both samples after applying this approach. Importantly, three side drilled holes and the back wall of the HNA sample are clearly enhanced in the processed image, with a mean 133% Contrast to Noise Ratio improvement when compared to a conventional TFM image