Signal Processing for NDE

Abstract

Nowadays, testing and evaluating of industrial equipment using nondestructive tests, is a fundamental step in the manufacturing process. The complexity and high costs of manufacturing industrial components, require examinations in some way about the quality and reliability of the specimens. However, it should be noted, that in order to accurately perform the nondestructive test, in addition to theoretical knowledge, it is also essential to have the experience and carefulness, which requires special courses and experience with theoretical education. Therefore, in the traditional methods, which are based on manual testing techniques and the test results depend on the operator, there is the possibility of an invalid inference from the test data. In other words, the accuracy of conclusion from the obtained data is dependent on the skill and experience of the operator. Thus, using the signal processing techniques for nondestructive evaluation (NDE), it is possible to optimize the methods of nondestructive inspection, and in other words, to improve the overall system performance, in terms of reliability and system implementation costs. In recent years, intelligent signal processing techniques have had a significant impact on the progress of nondestructive assessment. In other words, by automating the processing of nondestructive data and signals, and using the artificial intelligence methods, it is possible to optimize nondestructive inspection methods. Hence, improve overall system performance in terms of reliability and Implementation costs of the system. This chapter reviews the issues of intelligent processing of nondestructive testing (NDT) signals.

Artificial Intelligence, Machine Learning and Smart Technologies for Nondestructive Evaluation

“…General challenges in application of AI methods for NDE are that usually a sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required [11]. In addition, it should be noted that for accurate performance of AI techniques for NDE, an adequate level of theoretical and mathematical knowledge, experience in applying NDE techniques for inspection and material characterization, and carefulness is necessary [52].…”

A Past, Present, and Prospective Review on Microwave Nondestructive Evaluation of Composite Coatings

“…Moreover, wavelet transformation also has a better localization property. Overall, it is used to find the moment when an abrupt change occurred in the frequency domain at a certain period [76]. In addition, other reported researches have shown that wavelet transformation is the most successful method to use for feature extraction and selection [77,78].…”

“…However, some problems still arise in the training samples, and the corresponding classes are not always available. Hence, another method is known as unsupervised classification or clustering has been introduced [76]. Here, the classification is done by determining the likeness and grouping it into the same vector according to x feature vectors.…”

“…A classifier needs to be selected throughout the classification stage to maintain high accuracy and speed with low computational steps. Besides, the high reliability of classification in the NDT inspection system is also important to ensure clear results [76].…”