GB202202246D0 - Anti-leak system and methods - Google Patents

Anti-leak system and methods

Info

Publication number
GB202202246D0
GB202202246D0 GBGB2202246.1A GB202202246A GB202202246D0 GB 202202246 D0 GB202202246 D0 GB 202202246D0 GB 202202246 A GB202202246 A GB 202202246A GB 202202246 D0 GB202202246 D0 GB 202202246D0
Authority
GB
United Kingdom
Prior art keywords
methods
leak system
leak
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
GBGB2202246.1A
Other versions
GB2615800A (en
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Bristol
Original Assignee
University of Bristol
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Bristol filed Critical University of Bristol
Priority to GB2202246.1A priority Critical patent/GB2615800A/en
Publication of GB202202246D0 publication Critical patent/GB202202246D0/en
Priority to PCT/IB2023/051225 priority patent/WO2023135587A1/en
Publication of GB2615800A publication Critical patent/GB2615800A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M3/00—Investigating fluid-tightness of structures
    • G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
    • G01M3/04—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
    • G01M3/16—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means
    • G01M3/18—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means for pipes, cables or tubes; for pipe joints or seals; for valves; for welds; for containers, e.g. radiators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Examining Or Testing Airtightness (AREA)
GB2202246.1A 2022-01-17 2022-02-18 Anti-leak system and methods Pending GB2615800A (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
GB2202246.1A GB2615800A (en) 2022-02-18 2022-02-18 Anti-leak system and methods
PCT/IB2023/051225 WO2023135587A1 (en) 2022-01-17 2023-02-10 Anti-leak system and methods

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
GB2202246.1A GB2615800A (en) 2022-02-18 2022-02-18 Anti-leak system and methods

Publications (2)

Publication Number Publication Date
GB202202246D0 true GB202202246D0 (en) 2022-04-06
GB2615800A GB2615800A (en) 2023-08-23

Family

ID=80934631

Family Applications (1)

Application Number Title Priority Date Filing Date
GB2202246.1A Pending GB2615800A (en) 2022-01-17 2022-02-18 Anti-leak system and methods

Country Status (1)

Country Link
GB (1) GB2615800A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115654381A (en) * 2022-10-24 2023-01-31 电子科技大学 Water supply pipeline leakage detection method based on graph neural network

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021519433A (en) * 2018-03-28 2021-08-10 フラクタＦｒａｃｔａ Piping damage prediction
WO2021211785A1 (en) * 2020-04-17 2021-10-21 Siemens Industry, Inc. Systems and methods for detecting and predicting a leak in a pipe system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115654381A (en) * 2022-10-24 2023-01-31 电子科技大学 Water supply pipeline leakage detection method based on graph neural network

Also Published As

Publication number Publication date
GB2615800A (en) 2023-08-23

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