Application of Neural Networks for Classification of Eddy Current NDT Data

The inverse problem in nondestructiye evaluation involves the characterization of flaw parameters given a transducer response signal. In general the governing equations and boundary conditions describing the underlying physical phenomena are complex. Consequently analytical closed form solutions can be obtained only under. strong simplifying assumptions with regard to geometry and linearity of the problem. This precludes their use as direct inverse models for solving realistic NDT problems necessitating the need for using indirect inverse models based on pattern recognition algorithms. These inverse models classify the NDT signal as belonging to one of the classes of defects stored in a data bank as shown in Fig. 1

A New Approach for Practical Two Dimensional Data Fusion Utilizing a Single Eddy Current Probe

Interest in data fusion techniques have been growing in recent years due to the belief that a single NDE measurement may often be inadequate for providing sufficient information about the state of a test specimen. A variety of data fusion approaches have been proposed for combining results obtained by different methods, as well as different sensors, to provide comprehensive information about the material under test [1–4]. Techniques proposed to date range from blind superposition to approaches that involve the use of statistical and AI methods [5–7]

Time Scaling and Frequency Invariant Multiresolution Analysis of Ultrasonic NDE Signals

Nuclear power plant pipes are periodically inspected for possible cracks that occur in the heat-affected zones of welds. Intergranular stress corrosion cracks (IGSCC) are the most common type of cracks encountered particularly in stainless steel piping. Three major factors are required for the formation and propagation of IGSCCs, the tensile stress on the inner diameter of the weld region, a corrosive environment and a sensitized grain structure. When these flaws are not detected early enough, the consequences can be disastrous, and therefore the detection of IGSCCs is of significant interest to the nuclear industry

A Modified Differential Pickup Reflection Probe

In the eddy current inspection of multi-layered structures in aircrafts, the detection of flaws in the subsurface layers is a significant challenge and requires very sensitive probes. These flaws are often present in the vicinity of edges of the structures and hence the inspection probe should also be capable of discriminating the defect signal from the edge signal. Edges in samples under inspection give rise to very large eddy current signals thereby masking any signals that the probe may detect from a flaw close to the edge. This is referred to as the edge effect, differential flux signal on inside and outside of the driver coil. A thin ferromagnetic shield around the probe coil is included to enhance the sensitivity of this probe

An Improved Defect Classification Algorithm Based on Fuzzy Set Theory

The characterization of defects in materials constitutes a major area of research emphasis. Characterization schemes often involve mapping of the signal onto an appropriate feature domain. Defects are usually classified by segmenting the feature space and identifying the segment in which the feature vector is located. As an example Udpa and Lord [1] map differential eddy current impedance plane signals on to the feature space using the Fourier Descriptor approach. Doctor and Harrington [2] use the Fisher Linear Discriminant method to identify elements of the feature vector that demonstrate a statistical correlation with the nature of the defect. Mucciardi [3] uses the Adaptive Learning Network to build the feature vector. In all these cases defect classification is typically accomplished by categorizing the mapped feature vectors using Pattern Recognition methods employing either distance or likelihood functions [4]

Stress Characterization by Local Magnetic Measurements

The US natural gas transmission pipeline system consists in excess of 90,000 miles of gathering lines, 280,000 miles of transmission lines and 835,000 miles of distribution mains and lines. Since transmission lines transport natural gas at high pressures, the failure of a pipeline can result in catastrophic consequences. Sections of the transmission pipeline that have been in operation for several years are prone to the effects of corrosion and often suffer from mechanical damage. Pipelines are periodically inspected to assess their condition using a tool called “a pig.” Since the inspection of the transmission lines is an expensive procedure it is extremely important to infer information as accurately as possible about the condition of the pipeline

Synthetic Aperture Focusing Technique Using the Envelope Function for Ultrasonic Imaging

In traditional ultrasonic imaging systems, a transducer is scanned across the surface of a specimen at constant intervals. Synthetic aperture focusing techniques (SAFT) have been utilized extensively to process the RF data in order to enhance the signal-to-noise ratio of the image [1]. However, the implementation of the algorithm using sampled RF data has the disadvantage of requiring large memory and high-speed devices. These requirements can be reduced by using the envelope of the RF signal which involves processing the baseband signal. The envelope detection can be easily implemented as part of the receiver circuit

Optoelectronic system for implementation of iterative computer tomography algorithms

An optoelectronic tomographic reconstruction system utilizes spacial light modulators in charge couple devices to perform projection iterative reconstruction techniques and simultaneous iterative reconstruction techniques. A back projection processor uses a linear array of analog spacial light modulators in a cylindrical lens, an image detection array, and an image rotator. The image rotator smears a projected image at the same angle as the projection was taken. The back projection processor thus smears the projection back to the image space. An optoelectronic forward projection processor uses an spacial light modulator array, an image rotator, and an image detecting array. A reconstructed image displayed by the spacial light modulator is smeared by the rotator to forward project the reconstructed image on the image detecting array at the same angle as when the measured projection was taken. The forward projection processor thus smears the reconstructed image back into the projection space