Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pipe Flowrate Measurement01:28

Pipe Flowrate Measurement

1.2K
In pipe flow measurement, orifice, nozzle, and Venturi meters are commonly used to determine fluid flowrates by constricting the flow area, which increases fluid velocity and reduces pressure. This pressure difference, governed by Bernoulli's principle and adjusted for real-world conditions, is essential for calculating flowrate. Each meter type is suited to specific applications based on accuracy, efficiency, and compatibility with various flow conditions.
The orifice meter is a simple,...
1.2K
Pipe Flowrate Measurement: Problem Solving01:28

Pipe Flowrate Measurement: Problem Solving

818
A spray tank system is engineered to uniformly distribute a pest-control liquid across plants by using a pressurized mechanism. The tank, pressurized to 150 kPa, holds the pesticide at a height of 0.80 meters. Liquid flows from the tank through a 1.9 meter pipe with a diameter of 0.015 meters, angled at 0.698 radians, ultimately reaching a 0.007 meter nozzle that sprays the pesticide. Accurate calculation of the system's flow rate is crucial to ensure uniform application, and this is achieved...
818
Multiple Pipe Systems01:21

Multiple Pipe Systems

1.2K
Multipipe systems consist of complex configurations of interconnected pipes designed to transport fluids efficiently across intricate networks. They are essential in engineering applications requiring precise control over flow distribution, pressure, and head loss. They are categorized into series, parallel, loop, and network configurations, each distinguished by unique flow characteristics and applications.
Series Configuration
In a series configuration, fluid flows sequentially from one pipe...
1.2K
Minor Losses in Pipes01:25

Minor Losses in Pipes

1.9K
In pipe systems, minor losses refer to energy losses arising from components such as valves, bends, fittings, expansions, and other features that disrupt the steady flow of fluid. These disturbances cause energy dissipation through turbulence and resistance, which engineers quantify to manage system efficiency effectively.
Valves play a significant role in generating minor losses by obstructing or redirecting the fluid flow. When a valve is closed or partially closed, it restricts the flow...
1.9K
Single Pipe Systems01:24

Single Pipe Systems

431
In pipe flow analysis, problems are typically categorized into three types — Type I, Type II, and Type III — based on the known parameters and the desired outcome. Each type of problem addresses specific engineering requirements using fluid properties, pipe characteristics, and operational conditions.
In a Type I problem, fluid properties (density and viscosity), pipe characteristics (including diameter, length, and surface roughness), and the flow rate or average velocity are...
431
Major Losses in Pipes01:28

Major Losses in Pipes

1.9K
When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
Fluid flow can be classified as laminar or turbulent, primarily based on the Reynolds number. This dimensionless number reflects the relative influence of inertial to viscous...
1.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

DOT1L regulates dystrophin expression and is critical for cardiac function.

Genes & development·2011
Same author

The ribosomal intergenic spacer (IGS) region in Schistosoma japonicum: structure and comparisons with related species.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases·2011
Same author

Effects of intravesical liposome-mediated human beta-defensin-2 gene transfection in a mouse urinary tract infection model.

Microbiology and immunology·2011
Same author

A polyacrylamide microbead-integrated chip for the large-scale manufacture of ready-to-use esiRNA.

Lab on a chip·2011
Same author

Investigation on wide-band scattering of a 2-D target above 1-D randomly rough surface by FDTD method.

Optics express·2011
Same author

A cross-sectional study on posttraumatic impact among Qiang women in Maoxian County 1 year after the Wenchuan Earthquake, China.

Asia-Pacific journal of public health·2011

Related Experiment Video

Updated: Jan 16, 2026

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

658

Water pipe leakage detection method based on bi-sensor data fusion.

Meijia He1, Juan Li1, Yiqian Liu1

  • 1College of Communication Engineering, Jilin University, Changchun, China.

Water Research
|October 2, 2025
PubMed
Summary

This study introduces an improved bi-sensor Gramian angular field (IBGAF) and dual-channel multiscale feature fusion network (DMFFN) for reliable water pipe leak detection. The novel method enhances accuracy by fusing complementary sensor data, significantly improving pipeline operational status assessment.

Keywords:
Data fusionLeak detectionMulti-scale convolutional neural networkTime series imaging

More Related Videos

Measurements of Local Instantaneous Convective Heat Transfer in a Pipe - Single and Two-phase Flow
08:25

Measurements of Local Instantaneous Convective Heat Transfer in a Pipe - Single and Two-phase Flow

Published on: April 30, 2018

7.6K
Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

17.3K

Related Experiment Videos

Last Updated: Jan 16, 2026

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

658
Measurements of Local Instantaneous Convective Heat Transfer in a Pipe - Single and Two-phase Flow
08:25

Measurements of Local Instantaneous Convective Heat Transfer in a Pipe - Single and Two-phase Flow

Published on: April 30, 2018

7.6K
Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

17.3K

Area of Science:

  • Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Single sensor data in water supply pipe leakage detection offers limited insight into pipeline status, leading to high false detection rates.
  • Comprehensive pipeline operational status assessment is crucial for accurate leakage extent determination.

Purpose of the Study:

  • To propose a novel leakage detection method that leverages complementary information from two sensors to enhance detection reliability.
  • To improve the accuracy and reduce false positives in identifying water supply pipe leakages.

Main Methods:

  • Developed an Improved Bi-sensor Gramian Angular Field (IBGAF) to map two sensor data types into spherical coordinates, encoding signal information into RGB channels.
  • Introduced a Dual-channel Multiscale Feature Fusion Network (DMFFN) with a multi-scale convolutional architecture and attention mechanism for effective fusion of complementary signals.
  • Implemented a 2-D convolutional parallel feature fusion mechanism within the DMFFN for adaptive fusion of multi-scale information, reducing network complexity.

Main Results:

  • The proposed IBGAF and DMFFN method demonstrated significantly enhanced detection accuracy compared to single-class signal detection methods.
  • Experimental results confirmed the method's superior performance over other detection techniques.
  • The approach accurately recognized four distinct pipeline leakage conditions.

Conclusions:

  • The integration of IBGAF and DMFFN offers a robust solution for water supply pipe leakage detection.
  • The method effectively utilizes complementary sensor data, leading to improved reliability and accuracy in leak identification.
  • This approach represents a significant advancement in pipeline monitoring and maintenance technologies.