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Machine learning-assisted optical fiber specklegram sensor for early and spatially distributed water leak detection and localization.

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Title: Machine learning-assisted optical fiber specklegram sensor for early and spatially distributed water leak detection and localization.
Authors: Inalegwu, Ogbole Collins1 (AUTHOR) ocigwk@mst.edu, Roman, Muhammad1 (AUTHOR) mroman@mst.edu, Mumtaz, Farhan1 (AUTHOR) mfmawan@mst.edu, Zhang, Bohong1 (AUTHOR) bzdtx@mst.edu, Nambisan, Anand1 (AUTHOR) akn36d@mst.edu, Huang, Jie1 (AUTHOR) jieh@mst.edu
Source: Optical Engineering. May2025, Vol. 64 Issue 5, p56103-56103. 1p.
Subjects: Convolutional neural networks, Fiber optical sensors, Water leakage, Optical fiber networks, Leak detection
Abstract: We harness the change in boundary conditions of a no-core fiber (NCF) for high-sensitivity water leak detection and leak-spot localization, using a cost-effective charge-coupled device (CCD) camera and machine learning analysis. The CCD camera is utilized as an interrogation unit for capturing the specklegram images at the end-face of the NCF. Water leaks, which alter the ambient refractive index and thus the boundary conditions at the more sensitive uncoated sections of the NCF, induce noticeable shifts in specklegram images generated by multimodal interference within the NCF, and these shifts are detected using a convolutional neural network. Remarkably, the sensor exhibits high sensitivity, detecting water volumes as small as 0.1 mL and identifying leak spots 1 cm apart. Moreover, the presented simulation results support the experimental findings, enhancing the study's robustness and providing a comprehensive, low-cost, and efficient approach to leak detection. [ABSTRACT FROM AUTHOR]
Copyright of Optical Engineering is the property of SPIE - International Society of Optical Engineering 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.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 186052617
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Machine learning-assisted optical fiber specklegram sensor for early and spatially distributed water leak detection and localization.
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  Data: <searchLink fieldCode="AR" term="%22Inalegwu%2C+Ogbole+Collins%22">Inalegwu, Ogbole Collins</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ocigwk@mst.edu</i><br /><searchLink fieldCode="AR" term="%22Roman%2C+Muhammad%22">Roman, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mroman@mst.edu</i><br /><searchLink fieldCode="AR" term="%22Mumtaz%2C+Farhan%22">Mumtaz, Farhan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mfmawan@mst.edu</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Bohong%22">Zhang, Bohong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bzdtx@mst.edu</i><br /><searchLink fieldCode="AR" term="%22Nambisan%2C+Anand%22">Nambisan, Anand</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> akn36d@mst.edu</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Jie%22">Huang, Jie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jieh@mst.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Optical+Engineering%22">Optical Engineering</searchLink>. May2025, Vol. 64 Issue 5, p56103-56103. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Fiber+optical+sensors%22">Fiber optical sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Water+leakage%22">Water leakage</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+fiber+networks%22">Optical fiber networks</searchLink><br /><searchLink fieldCode="DE" term="%22Leak+detection%22">Leak detection</searchLink>
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  Label: Abstract
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  Data: We harness the change in boundary conditions of a no-core fiber (NCF) for high-sensitivity water leak detection and leak-spot localization, using a cost-effective charge-coupled device (CCD) camera and machine learning analysis. The CCD camera is utilized as an interrogation unit for capturing the specklegram images at the end-face of the NCF. Water leaks, which alter the ambient refractive index and thus the boundary conditions at the more sensitive uncoated sections of the NCF, induce noticeable shifts in specklegram images generated by multimodal interference within the NCF, and these shifts are detected using a convolutional neural network. Remarkably, the sensor exhibits high sensitivity, detecting water volumes as small as 0.1 mL and identifying leak spots 1 cm apart. Moreover, the presented simulation results support the experimental findings, enhancing the study's robustness and providing a comprehensive, low-cost, and efficient approach to leak detection. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Optical Engineering is the property of SPIE - International Society of Optical Engineering 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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      – Type: doi
        Value: 10.1117/1.OE.64.5.056103
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: 56103
    Subjects:
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Fiber optical sensors
        Type: general
      – SubjectFull: Water leakage
        Type: general
      – SubjectFull: Optical fiber networks
        Type: general
      – SubjectFull: Leak detection
        Type: general
    Titles:
      – TitleFull: Machine learning-assisted optical fiber specklegram sensor for early and spatially distributed water leak detection and localization.
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            NameFull: Inalegwu, Ogbole Collins
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            NameFull: Roman, Muhammad
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            NameFull: Zhang, Bohong
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            NameFull: Huang, Jie
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            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
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            – TitleFull: Optical Engineering
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