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Recovery of partial sensor failure for magnetic flux leakage sensors in pipeline inspection robots by block compressed sensing.

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Title: Recovery of partial sensor failure for magnetic flux leakage sensors in pipeline inspection robots by block compressed sensing.
Authors: Guo, Xiaoting1 (AUTHOR), Song, Huadong1 (AUTHOR), Zeng, Yanli1 (AUTHOR), Tang, Chaoqing2,3 (AUTHOR) billtang@hust.edu.cn, Chen, Honghe1 (AUTHOR), Zamora, Johnatan D.2 (AUTHOR)
Source: Nondestructive Testing & Evaluation. Feb2026, Vol. 41 Issue 2, p941-955. 15p.
Subjects: Pipeline inspection, Compressed sensing, Signal processing, Magnetic flux leakage, Fault tolerance (Engineering), Data integrity
Abstract: Partial sensor failures are common for pipeline inspection robots in oil and gas pipeline inspection due to the harsh working environment. These failures usually give partial values that are beyond the normal range or keep unchanged for an unusually long time, which stops the latter signal processing. Current interpolation-based methods fail to deal with continues block failure. This paper proposes one plug-in sensor failure recovery method that does not require any hardware update. The core failure recovery idea is modelling the data of sensor failure position as data loss, and the data from health sensors contain information of it in sparse domain by modelling the problem with compressed sensing (CS). This paper designs a virtual Bernoulli measurement matrix based on failure data locations and treat health data values as compressed sensing samples for estimating the failure data. Block-by-block estimation scheme is used to make use of the high spatial correlation of a local region. The experimental results on real pipeline inspection systems show our proposed CS-based measurement correction method is robust to up to 25% of error rate, and it has better performance than the traditional interpolation method for most typical signal features, e.g. 36% improvement in 2D coefficients with baseline for spiral weld under 30% of error rate. [ABSTRACT FROM AUTHOR]
Copyright of Nondestructive Testing & Evaluation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
  Group: Ti
  Data: Recovery of partial sensor failure for magnetic flux leakage sensors in pipeline inspection robots by block compressed sensing.
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  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Xiaoting%22">Guo, Xiaoting</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Huadong%22">Song, Huadong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zeng%2C+Yanli%22">Zeng, Yanli</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Chaoqing%22">Tang, Chaoqing</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> billtang@hust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Honghe%22">Chen, Honghe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zamora%2C+Johnatan+D%2E%22">Zamora, Johnatan D.</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Nondestructive+Testing+%26+Evaluation%22">Nondestructive Testing & Evaluation</searchLink>. Feb2026, Vol. 41 Issue 2, p941-955. 15p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Pipeline+inspection%22">Pipeline inspection</searchLink><br /><searchLink fieldCode="DE" term="%22Compressed+sensing%22">Compressed sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+flux+leakage%22">Magnetic flux leakage</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+tolerance+%28Engineering%29%22">Fault tolerance (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integrity%22">Data integrity</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Partial sensor failures are common for pipeline inspection robots in oil and gas pipeline inspection due to the harsh working environment. These failures usually give partial values that are beyond the normal range or keep unchanged for an unusually long time, which stops the latter signal processing. Current interpolation-based methods fail to deal with continues block failure. This paper proposes one plug-in sensor failure recovery method that does not require any hardware update. The core failure recovery idea is modelling the data of sensor failure position as data loss, and the data from health sensors contain information of it in sparse domain by modelling the problem with compressed sensing (CS). This paper designs a virtual Bernoulli measurement matrix based on failure data locations and treat health data values as compressed sensing samples for estimating the failure data. Block-by-block estimation scheme is used to make use of the high spatial correlation of a local region. The experimental results on real pipeline inspection systems show our proposed CS-based measurement correction method is robust to up to 25% of error rate, and it has better performance than the traditional interpolation method for most typical signal features, e.g. 36% improvement in 2D coefficients with baseline for spiral weld under 30% of error rate. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Nondestructive Testing & Evaluation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/10589759.2025.2477683
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 941
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      – SubjectFull: Pipeline inspection
        Type: general
      – SubjectFull: Compressed sensing
        Type: general
      – SubjectFull: Signal processing
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      – SubjectFull: Magnetic flux leakage
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      – SubjectFull: Fault tolerance (Engineering)
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      – SubjectFull: Data integrity
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    Titles:
      – TitleFull: Recovery of partial sensor failure for magnetic flux leakage sensors in pipeline inspection robots by block compressed sensing.
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            NameFull: Guo, Xiaoting
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            NameFull: Song, Huadong
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              Text: Feb2026
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              Y: 2026
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