Steven P. Neal

Flaw signature estimation in ultrasonic nondestructive evaluation using the Wiener filter with limited prior information

Flaw signals measured in ultrasonic testing include the effects of the measurements system and are corrupted by noise. The measurement system response is both bandlimited and frequency dependent within the bandwidth, resulting in measured signals which are blurred and distorted estimates of actual flaw signatures. The Wiener filter can be used to estimate the flaws scattering amplitude by removing the effect of the measurement system in the presence of noise. A method is presented for implementing an optimal form of the Wiener filter that requires only estimates of the noise distribution parameters. The theoretical error for scattering amplitude estimation, assuming various levels of available prior information, is analyzed. Three estimation techniques, one a maximum-likelihood based method and the other two residual-sum-of-squares methods, are formulated and tested. The results demonstrate that any of the three approaches could be used to optimally implement the alternative form of the Wiener filter with limited prior information.<<ETX>>

Grain noise power spectrum estimation for weak scattering polycrystalline materials using experimentally estimated backscatter coefficients: oblique incidence

Abstract In ultrasonic non-destructive evaluation, the tasks of flaw detection and characterization in polycrystalline materials are inhibited by grain noise. An estimate of the average power spectrum of the noise can be useful in assessing the probability of flaw detection and in suppressing the noise in order to enhance flaw detection and characterization. In this paper, a model-based approach is presented for estimating the average power spectrum associated with backscattered grain noise. The approach allows grain noise measurements made at one measurement system configuration to be used as a basis for estimating the noise power spectrum for different measurement system configurations. The modelling approach determines a noise power spectrum estimate by combining an estimate of the materials longitudinal-wave and transverse-wave backscatter coefficients with the distributed scatterer measurement system response functions for the measurement system configuration of interest. Power spectrum estimates are presented for oblique incidence testing, and these demonstrate the capability of the approach to handle variations in transducer type (planar versus focussed) water path, depth of penetration into the material and angle of interrogation.

The Measurement and Analysis of Acoustic Noise as a Random Variable

In ultrasonic nondestructive evaluation, experimental measurements of the scattered wave field resulting from sonification of a flaw are corrupted with acoustic noise. Acoustic noise results from non-flaw related scattering or reflection of the incident waves. In many probabilistic approaches to flaw detection, classification, and characterization, a stochastic model for a noise-corrupted flaw signal is utilized where acoustic noise is assumed to be an uncorrelated, Gaussian random variable with zero mean. In addition, it is assumed that an estimate of the average power spectra of the noise is available [1–3]. The goal of the work presented here was to measure and analyze acoustic noise as a random variable. Emphasis was placed on evaluating these assumptions and on estimating the average power spectra of the noise.

Experimental Evidence of Single and Multiple Scattering in Polycrystalline Materials

The backscatter coefficient, η(ω), is a material dependent acoustic parameter. As such, a reliable estimate of η(ω) may provide information concerning the microstructure and material properties for various engineering materials [1–4]. Current methods for properly estimating η(ω) are limited to cases in which the single scattering assumption is valid. Many models for grain noise also rely upon the validity of the single scattering assumption [5–9]. When attempting to validate models based upon the single scattering assumption, one must first verify that the single scattering assumption is valid. Therefore, a methodology is needed to determine if the single scattering assumption is in fact valid or if multiple scattering is significant.

WAVELETS AND THEIR APPLICATION TO DIGITAL SIGNAL PROCESSING IN ULTRASONIC NDE

As the use of digital based ultrasonic testing systems becomes more prevalent, there will be an increased emphasis on the development of digital signal processing techniques. In the past, various Fourier based digital signal processing approaches have been formulated and applied in the ultrasonic nondestructive evaluation (NDE) research community. In many cases, the inherent inability of Fourier methods to handle non-stationary signals has been exposed as the Fourier methods are applied to non-stationary ultrasonic signals. Our intent is to investigate the application of wavelet based signals processing techniques to a variety of problems in ultrasonic NDE. Wavelet methods have a number of potential advantage over Fourier methods including the inherent ability of wavelets to deal with non-stationary signals.

Identifying the Signal of Interest for Partially Overlapping Ultrasonic Echoes

The basic problem addressed in this paper is to discriminate between two signals that are at approximately the same time, but which originate at different echo sources. The proposed solution is to systematically perturb the field and discriminate between signals based on differences in amplitude variations between the two signals.

Spatial Correlation Coefficient Images for Ultrasonic Detection

In ultrasonics, image formation and detection are generally based on signal amplitude. In this paper, we introduce correlation coefficient images as a signal-amplitude independent approach for image formation. The correlation coefficients are calculated between A-scans digitized at adjacent measurement positions. In these images, defects are revealed as regions of high or low correlation relative to the background correlations associated with noise. Correlation coefficient and C-scan images are shown to demonstrate flat-bottom-hole detection in a stainless steel annular ring and crack detection in an aluminum aircraft structure.

ULTRASONIC NONDESTRUCTIVE EVALUATION APPLIED TO PROSTATE CANCER DETECTION

In this paper we will present initial results from an ongoing project at the University of Missouri-Columbia which focuses on prostate cancer detection using transrectal ultrasound. The overall project goal is the development of effective procedures for the detection, grading, and staging of prostate cancer. The goal of this initial study was lo ascertain whether acoustic parameters used in classical ultrasonic nondestructive evaluation (NDE) of engineering materials may be useful in identifying prostate cancer. The classical parameters speed of sound and attenuation coefficient were used along with a relatively new parameter in ultrasonic NDE, backscatter coefficient. The study was performed in vitro using human prostates which were obtained from radical prostatectomies. Each prostate was transversely sectioned to extract a 2 mm slice which was then scanned in an ultrasonic NDE research laboratory. Cancerous and benign regions of the prostate were subsequently identified on the tissue slice by a patholog...

Testing for Nongaussian Fluctuations in Grain Noise

In ultrasonic nondestructive evaluation (NDE), grain noise corrupts the scattered wave field from a flaw in a polycrystalline material. Many probabilistic approaches associated with flaw detection and characterization utilize stochastic models in which grain noise is assumed uncorrected and zero-mean Gaussian distributed. Typically, the Gaussian assumptions is justified via heuristic arguments based on the central limit theorem. This paper presents the kurtosis test and the Shapiro-Wilk W test as methods to quantitatively test time domain noise ensembles for deviations from Gaussian statistics. We will establish, through the application of these hypothesis tests to grain noise, a quantitative tool which can be used to consider “how Gaussian” grain noise signals must be for Gaussian noise based signal processing procedures to out perform alternative approaches.

An Application of Wavelet Signal Processing to Ultrasonic Nondestructive Evaluation

In this paper we present a flaw signature estimation approach which utilizes the Wiener filter [1–5] along with a wavelet based procedure [6–15] to achieve both deconvolution and reduction of acoustic noise. In related ealier work by Patterson et al. [6], the wavelet transform was applied to certain components of the Wiener filter, and coefficient chopping was used to reduce acoustic noise. In the approach that we present here, the wavelet transform is applied individually to the real part and to the imaginary part of the scattering amplitude estimate determined by application of a sub-optimal form of the Wiener filter. This wavelet transform takes the real and imaginary parts, respectively, from the typical Fourier frequency domain to a wavelet phase space. In this new space, the acoustic noise shows significant separation from the flaw signature making selective pruning of wavelet coefficients an effective means of reducing the acoustic noise. The final estimates of the real and imaginary parts of the scattering amplitude are determing via an inverse wavelet transform.