The use of ultrasonic waves in nondestructive evaluation is reviewed. Perspective is provided by a summary of the role played by nondestructive evaluation in the manufacture and utilization of structural components. Included are discussions of the methodologies for predicting part serviceability and of the constraints placed on the nondestructive measurement process by the mechanical designs and functions of structural components. The principles of active (ultrasonic) and passive (acoustic-emission) testing techniques are then reviewed. Specific examples of applications from several industries illustrate the capabilities and limitations of state-of-the-art techniques based on these principles. Detailed discussion is then given of present research and development activities designed to overcome these limitations, with particular emphasis on those areas best addressed by the tools of electrical engineering. Included are discussions of instrumentation, inspection performance modeling, flaw sizing techniques (including imaging systems), material property measurement techniques, and acousticemission techniques. The paper concludes with a consideration of important directions of future work.

Ultrasonics in nondestructive evaluation

This paper is concerned with the compression or dimensional reduction of a number of acoustic emission (AE) signals generated by damage to a structure, namely, a box girder from a bridge. The object of the exercise is to visualise the data and thereby associate clusters tothe measured AE bursts. These can be used tohelp interpret the signals. Two methods are used to reduce the dimension: principal component analysis and Sammon mapping.

Visualisation and Dimension Reduction of Acoustic Emission Data for Damage Detection

ix CHAPTER 1 – INTRODUCTION 1 1.1 BACKGROUND 1 1.2 PROJECT TASKS AND OBJECTIVES 2 1.3 SCOPE AND LIMITATIONS 4 1.4 CHAPTER LAYOUT 5 CHAPTER 2 – FUNDAMENTALS AND LITERATURE REVIEW 6 2.

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Acoustic emission monitoring and quantitative evaluation of damage in reinforced concrete members and bridges

Detection of wall thinning in piping systems in power generating plants is becoming more important as the time in service increases and deterioration by corrosion and erosion accumulates. Acousto-ultrasonic testing combines the advantages of ultrasonic inspection with the remote monitoring capability of acoustic emission for detection of this type of materials deterioration. While acousto-ultrasonic inspection has been applied in resin-matrix mites, attenuation parameters used to characterize acousto-ultrasonic signals are less discriminating in metals. This preliminary investigation showed the feasibility of using pattern recognition methods to effectively interpret acousto-ultrasonic signals from sections of metal piping that have been subjected to wall thinning.

Pattern recognition of ultrasonic signals for detection of wall thinning

This paper describes a new approach for classifying NDE waveforms Using this approach a set of matched filters is constructed one for each category of waveform, based on parameters from autoregressive models The method offers advantages in terms of hardware implementation over conventional pattern recognition approaches Feasibility is shown using computer generated data Results are then presented for real data from acoustic emission experiments

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1071&context=qnde

Classification of NDE Waveforms with Autoregressive Models

Three different pattern recognition techniques were applied to a data set consisting of acoustic emissions (AE) caused by crack growth and acoustic signals caused by extraneous noise sources. The purpose was to test the techniques for prescreening acoustic events and filtering out those that are produced by extraneous sources. The results were surprisingly good. Most of the noise waveforms used in this study cannot be distinguished from valid AE by visual examination; however, we were able to correctly classify 90% of the waveforms as either valid AE or noise using the least squares decision rule. Since the current application of AE data in nondestructive evaluation rely primarily on counting acoustic events, the waveforms caused by noise in the environment must be filtered out to avoid misinterpretation. The results of this investigation have convinced us that pattern recognition concepts can be used to design such a filter.

P.G. Doctor

Papers

Acoustic emission analysis using pattern recognition