Evidence map›Paper›PMID 37896727›Full record

ArticleSensors (Basel, Switzerland)2023

Optical Multimode Fiber-Based Pipe Leakage Sensor Using Speckle Pattern Analysis.

Jonathan Philosof, Yevgeny Beiderman, Sergey Agdarov, Yafim Beiderman, Zeev Zalevsky

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jonathan PhilosofThe Nanotechnology Center, Faculty of Engineering, Bar-Ilan University, Ramat-Gan 5290002, Israel.ORCID 0009-0008-1614-4910
Yevgeny BeidermanHolon Institute of Technology (HIT), Faculty of Electrical Engineering, Holon 5810201, Israel.
Sergey AgdarovThe Nanotechnology Center, Faculty of Engineering, Bar-Ilan University, Ramat-Gan 5290002, Israel.
Yafim BeidermanThe Nanotechnology Center, Faculty of Engineering, Bar-Ilan University, Ramat-Gan 5290002, Israel.
Zeev ZalevskyThe Nanotechnology Center, Faculty of Engineering, Bar-Ilan University, Ramat-Gan 5290002, Israel.ORCID 0000-0002-4459-3421

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Water is an invaluable resource quickly becoming scarce in many parts of the world. Therefore, the importance of efficiency in water supply and distribution has greatly increased. Some of the main tools for limiting losses in supply and distribution networks are leakage sensors that enable real-time monitoring. With fiber optics recently becoming a commodity, along with the sound advances in computing power and its miniaturization, multipurpose sensors relying on these technologies have gradually become common. In this study, we explore the development and testing of a multimode optic-fiber-based pipe monitoring and leakage detector based on statistical and machine learning analyses of speckle patterns captured from the fiber's outlet by a defocused camera. The sensor was placed inside or over a PVC pipe with covered and exposed core configurations, while 2 to 8 mm diameter pipe leaks were simulated under varied water flow and pressure. We found an overall leak size determination accuracy of 75.8% for a 400 µm covered fiber and of 68.3% for a 400 µm exposed fiber and demonstrated that our sensor detected pipe bursts, outside interventions, and shocks. This result was consistent for the sensors fixed inside and outside the pipe with both covered and exposed fibers.

Indexed as

leak detectionmachine learning (ML)optic fiber sensorspeckle pattern

Identifiers

PMID37896727
PMCPMC10610934

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.