eprintid: 12314
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/23/14
datestamp: 2023-11-10 03:26:51
lastmod: 2023-11-10 03:26:51
status_changed: 2023-11-10 01:17:23
type: article
metadata_visibility: show
creators_name: Mohd Jaafar, N.S.
creators_name: Aziz, I.A.
creators_name: Jaafar, J.
creators_name: Mahmood, A.K.
title: An approach of filtering to select IMFs of EEMD in signal processing for acoustic emission AE sensors
ispublished: pub
keywords: Acoustic emission testing; Acoustic noise; Acoustic wave propagation; Computation theory; Frequency domain analysis; Functions; Gas pipelines; Intelligent systems; Leakage (fluid); Mixing; Petroleum transportation; Pipeline corrosion; Processing, Empirical Mode Decomposition; Ensemble empirical mode decomposition; Intrinsic Mode functions; Mode mixing; Noise contamination; Selective accuracy of IMF, Signal processing
note: cited By 5; Conference of 2nd Computational Methods in Systems and Software, CoMeSySo 2018 ; Conference Date: 12 September 2018 Through 14 September 2018; Conference Code:217959
abstract: The pipeline system is the important part in the media transportation for oil and gas transmission but due to weak maintenance, it leads to the corrosion, leakage stresses and mechanical damage of oil and gas pipelines. The signal processing is used to decompose the raw signal and analysis will be in time-frequency domain. 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 two data sets from the Acoustic Emission Sensors AE, signal processing flows have been presented in this paper. Wave propagation in the pipeline is a key parameter in acoustic method when the leak occurs. More experiments and simulation need to be carried out to get more result for leakage signature and localisation of defect. © Springer Nature Switzerland AG. 2019.
date: 2019
publisher: Springer Verlag
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053603172&doi=10.1007%2f978-3-030-00184-1_10&partnerID=40&md5=5e12f802e220f2ef027c60ca5c6eb62c
id_number: 10.1007/978-3-030-00184-1₁₀
full_text_status: none
publication: Advances in Intelligent Systems and Computing
volume: 860
pagerange: 100-111
refereed: TRUE
isbn: 9783030001834
issn: 21945357
citation:   Mohd Jaafar, N.S. and Aziz, I.A. and Jaafar, J. and Mahmood, A.K.  (2019) An approach of filtering to select IMFs of EEMD in signal processing for acoustic emission AE sensors.  Advances in Intelligent Systems and Computing, 860.  pp. 100-111.  ISSN 21945357