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Signal Processing for NDE

  • Masoud Vejdannik
  • Ali Sadr
  • Victor Hugo C. de Albuquerque
  • João Manuel R. S. TavaresEmail author
Reference work entry

Abstract

Testing and evaluating of industrial equipment using nondestructive tests are fundamental steps 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 experience and care, which require 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 a 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, 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 artificial intelligence methods, it is possible to optimize nondestructive inspection methods, hence improving the 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.

Notes

Acknowledgments

VHCA acknowledges the sponsorship from the Brazilian National Council for Research and Development (CNPq) via Grant No. 301928/2014-2. JMRST gratefully acknowledges the funding of Project NORTE-01-0145-FEDER-000022 – SciTech – Science and Technology for Competitive and Sustainable Industries, co-financed by “Programa Operacional Regional do Norte” (NORTE2020), through “Fundo Europeu de Desenvolvimento Regional” (FEDER).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Masoud Vejdannik
    • 1
  • Ali Sadr
    • 1
  • Victor Hugo C. de Albuquerque
    • 2
  • João Manuel R. S. Tavares
    • 3
    Email author
  1. 1.School of Electrical EngineeringIran University of Science and Technology (IUST)Narmak, TehranIran
  2. 2.Programa de Pós Graduação em Informática AplicadaUniversidade de Fortaleza (UNIFOR)FortalezaBrazil
  3. 3.Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de EngenhariaUniversidade do PortoPortoPortugal

Section editors and affiliations

  • Ida Nathan
    • 1
  • Norbert Meyendorf
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of AkronAkronUSA
  2. 2.Center for Nondestructive EvaluationIowa State UniversityAmesUSA

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