Determination of the descriptors for the design of a classifier that allows the detection of loss of material in metal sheets based on signals of non-destructive tests

Hernando Gonzalez, Carlos Arizmendi, Javier Quintero, Mario A. Quintero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The article proposes a methodology to determine appropriate descriptors for the design of a classifier based on neural networks that allow the detection of loss of material in metal pipes based on nondestructive testing signals of Magnetic Flux Leakage (MFL). For this it has been proposed a method which consists of two stages: the first, corresponding to the signal processing, the Discrete Wavelet Transform (DWT) transform is used to implement a nonlinear threshold filtering or Shrinkage and correction baseline which seeks to eliminate or mitigate the different types of noise or phenomena found in the signal that make difficult the process of extracting relevant information to the subsequent detection of loss of material. In the second, corresponding to the design of the classifier, it seeks to identify a window width and descriptors in the time domain and the Power Spectral Density (PSD) to characterize the signal and differentiate areas of metal loss or no metal loss.

Original languageEnglish
Title of host publication2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages122-127
Number of pages6
ISBN (Electronic)9781538669877
DOIs
StatePublished - 25 Jun 2018
Event2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018 - Chengdu, China
Duration: 26 May 201828 May 2018

Publication series

Name2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018

Conference

Conference2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018
Country/TerritoryChina
CityChengdu
Period26/05/1828/05/18

Keywords

  • Artificial neural networks
  • Discrete wavelet transform
  • Magnetic flux leakage
  • Power spectral density

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