Advanced eddy current test signal analysis for steam generator tube defect classification and characterization

Eddy Current Testing (ECT) is a Non-Destructive Examination (NDE) technique that is widely used in power generating plants (both nuclear and fossil) to test the integrity of heat exchanger (HX) and steam generator (SG) tubing. Specifically for this research, laboratory-generated, flawed tubing data were examined The tubing data were acquired from the EPRI NDE Center, Charlotte, NC. The data are catalogued in the Performance Demonstration Database (POD) which is used as a training manual for certification. The specific subset of the data used in this dissertation has an Examination Technique Specification Sheet (ETSS) and a blueprint of the flawed tube specimens. The purpose of this dissertation is to develop and implement an automated method for the classification and an advanced characterization of defects in HX and SG tubing. These two improvements enhanced the robustness of characterization as compared to traditional bobbin-coil ECT data analysis methods. A more robust classification and characterization of the tube flaw insitu (while the SG is on-line but not when the plant is operating), should provide valuable information to the power industry. The following is a summary of the original contributions of this dissertation research. 1. Development of a feature extraction program acquiring relevant information from both the mixed, absolute and differential ECTD Flaw Signal (ECTDFS). 2. Application of the Continuous Wavelet Transformation (CWT) to extract more information from the mixed, complex differential ECTDFS. 3. Utilization of Image Processing (IP) techniques to extract the information contained in the generated CWT. 4. Classification of the ECTDFSs, using the compressed feature vector and a Bayes classification system. 5. Development of an upper bound for the probability of classification error, using the Bhattacharyya distance, for the Bayesian classification. 6. Tube defect characterization based on the classified flaw-type to enhance characterization 7. Development of a diagnostic software system EddyC and user\u27s guide. The important results of the application of the method are listed. The CWT contains at least enough information to correctly classify the flaws 64% of the time using the IP features. The Bayes classification system, using only the CWT generated features (after PCA compression), correctly identified 64% of the ECTD flaws. The Bayes classification system correctly identified 7 5% of the ECTD flaws using cross validation utilizing all the generated features after PCA compression. Initial template matching results (from the PDD database) yielded correct classification of 69%. The B-distances parallel and bound the percent misclassified cases. The calculated B-distance for 15 PCs were O and 14.22% bounding the 1.1% incorrectly classified. But, these Gaussian-based calculated B-distances may be inaccurate due to non-Gaussian features. The number of outliers seems to have an inverse relationship with the number of misclassifications. Characterization yielded an average error of 12.76 %. This excluded the results from flaw-type 1 (Thinning). The following are the conclusions reached from this research. A feature extraction program acquiring relevant information from both the mixed, absolute and differential data was successfully implemented. The CWT was utilized to extract more information from the mixed, complex differential data. Image Processing techniques used to extract the information contained in the generated CWT, classified the data with a high success rate. The data were accurately classified, utilizing the compressed feature vector and using a Bayes classification system. An estimation of the upper bound for the probability of error, using the Bhattacharyya distance, was successfully applied to the Bayesian classification. The classified data were separated according to flaw-type (classification) to enhance characterization. The characterization routine used dedicated, flaw-type specific ANNs that made the characterization of the tube flaw more robust. The inclusion of outliers may help complete the feature space so that classification accuracy is increased. Given that the eddy current test signals appear very similar, there may not be sufficient information to make an extremely accurate (\u3e 95%) classification or an advanced characterization using this system. It is necessary to have a larger database fore more accurate system learning

A Function of Time, Frequency, Lag, and Doppler

In signal processing, four functions of one variable are commonly used. They are the signal in time, the spectrum, the auto-correlation function of the signal, and the auto-correlation function of the spectrum. The variables of these functions are denoted, respectively, as time, frequency, lag, and doppler. In time-frequency analysis, these functions of one variable are extended to quadratic functions of two variables. In this paper, we investigate a method for creating quartic functions of three of these variables and also a quartic function of all four variables. These quartic functions provide a meaningful representation of the signal that goes beyond the well known quadratic functions. The quartic functions are applied to the design of signal-adaptive kernels for the Cohen class and shown to provide improvements over previous methods. Corresponding Author Jeffrey C. O'Neill Laboratoire de Physique Ecole Normale Sup'erieure 46 All'ee d'Italie 69364 Lyon Cedex 07 FRANCE Tel: (+33) 4 ..

Shift covariant time-frequency distributions of discrete signals.

Given a signal, one can readily see how the energy of the signal is distributed in time. By computing the Fourier transform one can readily see how the energy of the signal is distributed in frequency. However, neither representation indicates how the energy of the signal is distributed in time and frequency. Time-frequency analysis is the study of how to compute a function that represents the energy distribution of the signal simultaneously in time and frequency. This dissertation investigates the implementation of Cohen's class for discrete signals and a new quartic time-frequency distribution (TFD). Most TFDs used today are members of a class called Cohen's class. Cohen's class can be characterized as including all quadratic TFDs that are covariant to time shifts and frequency shifts. Cohen's class has been formulated for continuous signals and the extension of the theory to sampled signals is not straightforward. In signal processing, one uses the Fourier transform, the discrete-time Fourier transform, the Fourier series, and the discrete Fourier transform to analyze four types of signals. Cohen's class of TFDs and the Fourier transform are both applied to continuous signals. Previous work has tried to impose the properties of Cohen's class onto discrete classes of TFDs. Since the properties of the Fourier transform and the discrete-time Fourier transform are not identical, this dissertation proposes that the properties of TFDs of continuous and discrete signals should also not be identical. With this idea in mind, classes of TFDs are defined and investigated that correspond to the other three types of Fourier transforms. Recent work by several authors has investigated TFDs of higher than quadratic order. This dissertation creates a quartic function of the signal, called the Q function, that is fundamentally different from previous higher order TFDs. The Q function represents the signal as a function of time, frequency, lag, and doppler, and has many interesting properties and relationships with other functions in time-frequency analysis. The Q function is applied to create signal-adaptive kernels that vary over time and frequency. The kernels generated by the Q function extend and improve upon previous work for creating signal-adaptive kernels.Ph.D.Applied SciencesElectrical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/130320/2/9722054.pd