A prior knowledge based optimal Wiener filtering approach to ultrasonic scattering amplitude estimation

Abstract

, "A prior knowledge based optimal Wiener filtering approach to ultrasonic scattering amplitude estimation " (1988). Retrospective Theses and Dissertations. 8877. http://lib.dr.iastate.edu/rtd/8877 INFORMATION TO USERSThe most advanced technology has been used to photo graph and reproduce this manuscript from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion.Oversize materials (e.g., maps, drawings, charts) are re produced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. These are also available as one exposure on a standard 35mm slide or as a 17" x 23" black and white photographic print for an additional charge.Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. Signature was redacted for privacy.Signature was redacted for privacy.Signature was redacted for privacy. The work reported here is associated with flaw characterization.This work is motivated from an engineering perspective by considering two fundamentally different approaches to life prediction that can be found in fatigue of materials and structures. In a simplistic sense, a "safe life"or "zero defects" fatigue life prediction approach (Coffin and Tiffany 1976) assumes that once a flaw is macroscopic, i.e., once a flaw has grown to a size which can be detected by NDE techniques, failure is imminent and the component must be taken out of service. With this approach, qualitative NDE indicating the presence or absence of a flaw provides sufficient information to determine if a component should be retired. An alternative approach, which is sometimes referred to as a "damage tolerance" approach, is based on a fracture mechanics methodology (Coffin and Tiffany 1976, Rolfe and Barsom 1977). With this approach, instead of assuming that all flawed components must be taken out of service, the 2 severity of the flaw is assessed and a more rational determination of the remaining life of the component is made. To implement this approach, quantitative flaw characterization is required. As a minimum...

Ultrasonic inspection of drinking water mains

“…Thus, the transfer function has a particular value for each reflected wave, in this case, the front wall reflection and the back-wall reflection. The model presented by Neal et al [32] is…”

“…The transfer function ℎ( ) is the system response and it considers the voltage input to the sensor, the response from the electronics, the response from the transducer and the propagation effects of sound waves in the material (diffraction, attenuation, interface of mediums, etc.) [32][33][34][35]. The noise components can be grouped in a single term ( ).…”

“…The noise components can be grouped in a single term ( ). Solving for ( ) directly does not give meaningful results due to the mathematical ill posed problem [32]. ( ) is solved from a statistical point of view where ( ) and ( ) are uncorrelated Gaussian random variables with known variance and mean.…”

“…Thus, ( ) is a random Gaussian random variable as well. The transfer function ( ) is a non-random known value [32]. The estimated value of can be obtained from the maximum value of the probability density function ( ⁄ ) if this is a Gaussian random variable.…”

“…The estimated value of can be obtained from the maximum value of the probability density function ( ⁄ ) if this is a Gaussian random variable. Details of the derivation can be found in literature [32]. The estimated value of is obtained in the frequency domain…”