Machine Learning-Assisted Optical Fiber Specklegram Sensor For Early And Spatially Distributed Water Leak Detection And Localization

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.

Department(s)

Electrical and Computer Engineering

Comments

Tertiary Education Trust Fund, Grant None

Keywords and Phrases

convolutional neural network; no-core fiber; optical fiber sensors; speckle-gram; water leakage

International Standard Serial Number (ISSN)

1560-2303; 0091-3286

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Society of Photo-optical Instrumentation Engineers, All rights reserved.

Publication Date

01 May 2025

Link to Full Text

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