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Online data compression of MFL signals for pipeline inspection
Published in
2012
Volume: 50
   
Pages: 1 - 9
Abstract
The paper presents a novel three-stage algorithm for online compression of magnetic flux leakage (MFL) signals that are acquired in inspection of oil and gas pipelines. In the first stage, blocks of MFL signal are screened for useful information using a semi-robust statistical measure, Mean Absolute Deviation (μAD). The study presents guidelines for selecting a block size to deliver robust screening and efficient compression ratios. In the second stage, a multivariate approach is used to compress the data across sensors using Principal Component Analysis (PCA). The second stage is invoked only when an anomaly is detected by sufficiently large number of sensors. In the third stage, the signal is further compressed within each sensor (univariate approach) using Discrete Wavelet Transform (DWT). Implementation on real-time MFL signals demonstrates the algorithms ability to achieve high compression ratios with low Normalized Mean Square Error (NMSE) while being fairly robust to baseline shifts. © 2011 Elsevier Ltd. All rights reserved.
About the journal
JournalNDT and E International
ISSN09638695
Open AccessNo
Concepts (17)
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    BLOCK SIZES
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    HIGH COMPRESSION RATIO
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    MAGNETIC FLUX LEAKAGE
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    MEAN ABSOLUTE DEVIATIONS
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    MULTIVARIATE APPROACH
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    NORMALIZED MEAN SQUARE ERROR
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    OIL-AND-GAS PIPELINES
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    PIPELINE INSPECTION
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    STATISTICAL MEASURES
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    UNIVARIATE
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    Algorithms
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    Discrete wavelet transforms
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    Inspection
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    Magnetic leakage
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    Principal component analysis
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    Sensors
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    Data compression