relation: https://khub.utp.edu.my/scholars/12167/
title: Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature
creator: Jaafar, N.S.M.
creator: Aziz, I.A.
creator: Hasan, M.H.B.
creator: Mahmood, A.K.
description: The pipelines are used for transporting fluids and it is an important part of the media transportation for oil and gas. However, as pipelines are often spread across vast distances and carry certain hazardous substances, the chances for accidents such as leakage accidents in oil and gas pipelines are increased. Variety of factors lead to pipeline leakage accidents such as corrosion, vibration and other impacts affecting the safe operation of pipelines. Pipelines leakages cause both loss of product and as well as environmental damage. Acoustic emissions sensors have recently emerged as a promising tool for long distance pipeline monitoring due to the acoustic emission sensors advantages of high accuracy and low loss per distance. The signal processing is used to decompose the raw signal and the pre-processed signal will be analyzed in the time-frequency domain. Several existing signals processing methods such as Fourier Transform, Wavelet Transform can be used for extracting useful information. The parameters of Empirical Mode Decomposition EMD show promising results. The promising results in terms of accuracy of selections IMFs and analysis of time-frequency domain. The selected of Intrinsic Mode Functions IMFs IMFs are analyzed in the time domain by using two parameters which are standard deviation and variance. The selected IMFs are obtained to reveal the leakage and no leakage signatures of the pipeline. © Springer Nature Switzerland AG 2019.
publisher: Springer Verlag
date: 2019
type: Article
type: PeerReviewed
identifier:   Jaafar, N.S.M. and Aziz, I.A. and Hasan, M.H.B. and Mahmood, A.K.  (2019) Parameter calculation in time analysis for the approach of filtering to select IMFs of EMD in AE sensors for leakage signature.  Advances in Intelligent Systems and Computing, 985.  pp. 139-146.  ISSN 21945357     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065903839&doi=10.1007%2f978-3-030-19810-7_14&partnerID=40&md5=1ed60dfdeefb9c30762b5ea13bb377b1
relation: 10.1007/978-3-030-19810-7₁₄
identifier: 10.1007/978-3-030-19810-7₁₄