An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel

Number of existing signal processing methods can be used for extracting useful information. However, the problem of signal processing method, essential to highlight the wanted information and attenuate the undesired signal is trivial. Several signal processing methods have been implemented to solve...

Full description

Main Authors: Jaafar, N.S.M., Aziz, I.A., Jaafar, J., Mahmood, A.K.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23528 /
Published: Springer Verlag 2019
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053632484&doi=10.1007%2f978-3-030-00211-4_23&partnerID=40&md5=60901aaf84aaa526f8cb1c522a1574ff
http://eprints.utp.edu.my/23528/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.23528
recordtype eprints
spelling utp-eprints.235282021-08-19T07:57:33Z An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel Jaafar, N.S.M. Aziz, I.A. Jaafar, J. Mahmood, A.K. Number of existing signal processing methods can be used for extracting useful information. However, the problem of signal processing method, essential to highlight the wanted information and attenuate the undesired signal is trivial. Several signal processing methods have been implemented to solve this issue. Research using Empirical Mode Decomposition (EMD) algorithm shows promising results in comparison to other signal processing methods, especially in the accuracy showing the relationship between signal energy and time � frequency distribution by represents series of the stationary signals with different amplitudes and frequency bands. However, this EMD algorithm will still have noise contamination that may compromise the accuracy of the signal processing to highlight the wanted information. It is because the mode mixing phenomenon in the Intrinsic Mode Function�s (IMF) due to the undesirable signal with the mix of additional noise. There is still room for the improvement in the selective accuracy of the sensitive IMF after decomposition that can influence the correctness of feature extraction of the oxidized carbon steel. Using four datasets, analysis parameters of the Ensemble Empirical Mode Decomposition (EEMD) algorithm has been conducted. © Springer Nature Switzerland AG. 2019. Springer Verlag 2019 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053632484&doi=10.1007%2f978-3-030-00211-4_23&partnerID=40&md5=60901aaf84aaa526f8cb1c522a1574ff Jaafar, N.S.M. and Aziz, I.A. and Jaafar, J. and Mahmood, A.K. (2019) An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel. Advances in Intelligent Systems and Computing, 859 . pp. 255-273. http://eprints.utp.edu.my/23528/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Number of existing signal processing methods can be used for extracting useful information. However, the problem of signal processing method, essential to highlight the wanted information and attenuate the undesired signal is trivial. Several signal processing methods have been implemented to solve this issue. Research using Empirical Mode Decomposition (EMD) algorithm shows promising results in comparison to other signal processing methods, especially in the accuracy showing the relationship between signal energy and time � frequency distribution by represents series of the stationary signals with different amplitudes and frequency bands. However, this EMD algorithm will still have noise contamination that may compromise the accuracy of the signal processing to highlight the wanted information. It is because the mode mixing phenomenon in the Intrinsic Mode Function�s (IMF) due to the undesirable signal with the mix of additional noise. There is still room for the improvement in the selective accuracy of the sensitive IMF after decomposition that can influence the correctness of feature extraction of the oxidized carbon steel. Using four datasets, analysis parameters of the Ensemble Empirical Mode Decomposition (EEMD) algorithm has been conducted. © Springer Nature Switzerland AG. 2019.
format Article
author Jaafar, N.S.M.
Aziz, I.A.
Jaafar, J.
Mahmood, A.K.
spellingShingle Jaafar, N.S.M.
Aziz, I.A.
Jaafar, J.
Mahmood, A.K.
An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel
author_sort Jaafar, N.S.M.
title An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel
title_short An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel
title_full An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel
title_fullStr An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel
title_full_unstemmed An approach of filtering to select IMFs of EEMD in acoustic emission AE sensors for oxidized carbon steel
title_sort approach of filtering to select imfs of eemd in acoustic emission ae sensors for oxidized carbon steel
publisher Springer Verlag
publishDate 2019
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053632484&doi=10.1007%2f978-3-030-00211-4_23&partnerID=40&md5=60901aaf84aaa526f8cb1c522a1574ff
http://eprints.utp.edu.my/23528/
_version_ 1741196690073845760
score 11.62408

Similar Items