EP3596439A4 - Fatigue crack growth prediction - Google Patents

Fatigue crack growth prediction Download PDF

Info

Publication number
EP3596439A4
EP3596439A4 EP18766900.7A EP18766900A EP3596439A4 EP 3596439 A4 EP3596439 A4 EP 3596439A4 EP 18766900 A EP18766900 A EP 18766900A EP 3596439 A4 EP3596439 A4 EP 3596439A4
Authority
EP
European Patent Office
Prior art keywords
crack growth
fatigue crack
growth prediction
prediction
fatigue
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
EP18766900.7A
Other languages
German (de)
French (fr)
Other versions
EP3596439A1 (en
Inventor
Siyu Wu
Alireza Dibazar
Craig Wesley STEVENS
Lauren Ashley VAHLDICK
Timothy Ryan Greene
Louis Christopher Nucci
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.)
General Electric Co
Original Assignee
General Electric Co
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
Priority claimed from US15/910,412 external-priority patent/US20180260720A1/en
Application filed by General Electric Co filed Critical General Electric Co
Publication of EP3596439A1 publication Critical patent/EP3596439A1/en
Publication of EP3596439A4 publication Critical patent/EP3596439A4/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
    • G01M5/0033—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining damage, crack or wear
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble 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
    • G01M15/00—Testing of engines
    • G01M15/14—Testing gas-turbine engines or jet-propulsion engines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/76—Architectures of general purpose stored program computers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Geometry (AREA)
  • Computer Hardware Design (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Computational Linguistics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Automation & Control Theory (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
EP18766900.7A 2017-03-13 2018-03-06 Fatigue crack growth prediction Pending EP3596439A4 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201762470539P 2017-03-13 2017-03-13
US15/910,412 US20180260720A1 (en) 2017-03-13 2018-03-02 Fatigue Crack Growth Prediction
PCT/US2018/021011 WO2018169722A1 (en) 2017-03-13 2018-03-06 Fatigue crack growth prediction

Publications (2)

Publication Number Publication Date
EP3596439A1 EP3596439A1 (en) 2020-01-22
EP3596439A4 true EP3596439A4 (en) 2021-03-31

Family

ID=63523228

Family Applications (1)

Application Number Title Priority Date Filing Date
EP18766900.7A Pending EP3596439A4 (en) 2017-03-13 2018-03-06 Fatigue crack growth prediction

Country Status (3)

Country Link
EP (1) EP3596439A4 (en)
CN (1) CN110431395A (en)
WO (1) WO2018169722A1 (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11397134B2 (en) * 2018-12-03 2022-07-26 Raytheon Technologies Corporation Intelligent learning device for part state detection and identification
CN111914353B (en) * 2020-07-13 2024-01-19 中广核核电运营有限公司 Rotor low-cycle fatigue loss detection method, device and computer equipment
CN113065224B (en) * 2021-03-05 2022-05-17 天津大学 Deep sea pipeline crack propagation monitoring and reliability evaluation method based on image recognition
CN113343530B (en) * 2021-06-11 2022-05-06 清华大学 Design method and device for controlling fatigue damage fracture of space station shell structure
CN115169240A (en) * 2022-07-26 2022-10-11 南京理工大学 TiAl alloy fatigue crack propagation life prediction method based on machine learning

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050096873A1 (en) * 2002-12-30 2005-05-05 Renata Klein Method and system for diagnostics and prognostics of a mechanical system
US20130245879A1 (en) * 2012-03-05 2013-09-19 Eads Construcciones Aeronauticas, S.A., Sociedad Unipersonal Method and system for monitoring a structure

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1143242B1 (en) * 1994-08-31 2003-07-16 Honeywell Inc. Remote self-powered structure monitor method
US7006947B2 (en) * 2001-01-08 2006-02-28 Vextec Corporation Method and apparatus for predicting failure in a system
US8768657B2 (en) * 2006-01-12 2014-07-01 Jentek Sensors, Inc. Remaining life prediction for individual components from sparse data
JP5193714B2 (en) * 2008-07-18 2013-05-08 Ｊｘ日鉱日石エネルギー株式会社 Piping crack diagnostic device and piping crack diagnostic method
CN102128880A (en) * 2010-01-12 2011-07-20 上海工程技术大学 Crack shape inversion method
JP5384429B2 (en) * 2010-05-21 2014-01-08 日本電信電話株式会社 Crack detection apparatus, crack detection method and program for concrete structure image
DE102011105182A1 (en) * 2011-06-17 2012-12-20 Albert-Ludwigs-Universität Freiburg A method of providing a predictive model for crack detection and a method of crack detection on a semiconductor structure
WO2013040315A1 (en) * 2011-09-16 2013-03-21 Sentient Corporation Method and system for predicting surface contact fatigue life
US9347288B2 (en) * 2011-11-15 2016-05-24 Halliburton Energy Services, Inc. Modeling operation of a tool in a wellbore
US9792555B2 (en) * 2013-01-04 2017-10-17 Siemens Energy, Inc. Probabilistic modeling and sizing of embedded flaws in ultrasonic nondestructive inspections for fatigue damage prognostics and structural integrity assessment
CN104515786B (en) * 2015-01-08 2018-05-11 北京科技大学 The detection and analysis method that metal casting fatigue process internal flaw develops

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050096873A1 (en) * 2002-12-30 2005-05-05 Renata Klein Method and system for diagnostics and prognostics of a mechanical system
US20130245879A1 (en) * 2012-03-05 2013-09-19 Eads Construcciones Aeronauticas, S.A., Sociedad Unipersonal Method and system for monitoring a structure

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
BECERRA VILLANUEVA J A ET AL: "A methodology for cracks identification in large crankshafts", MECHANICAL SYSTEMS AND SIGNAL PROCESSING, ELSEVIER, AMSTERDAM, NL, vol. 25, no. 8, 10 February 2011 (2011-02-10), pages 3168 - 3185, XP028288855, ISSN: 0888-3270, [retrieved on 20110312], DOI: 10.1016/J.YMSSP.2011.02.018 *
MOHAMMED A A ET AL: "Crack detection in a rotating shaft using artificial neural networks and PSD characterisation", MECCANICA, KLUWER ACADEMIC PUBLISHERS, DORDRECHT, NL, vol. 49, no. 2, 17 August 2013 (2013-08-17), pages 255 - 266, XP035366125, ISSN: 0025-6455, [retrieved on 20130817], DOI: 10.1007/S11012-013-9790-Z *
See also references of WO2018169722A1 *
TAO YU ET AL: "Crack fault identification in rotor shaft with artificial neural network", NATURAL COMPUTATION (ICNC), 2010 SIXTH INTERNATIONAL CONFERENCE ON, IEEE, PISCATAWAY, NJ, USA, 10 August 2010 (2010-08-10), pages 1629 - 1634, XP031761962, ISBN: 978-1-4244-5958-2 *
ZHANG WEI ET AL: "An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation", MATERIALS, vol. 9, no. 6, 17 June 2016 (2016-06-17), pages 483, XP055777138, Retrieved from the Internet <URL:https://www.researchgate.net/publication/304067877_An_Artificial_Neural_Network-Based_Algorithm_for_Evaluation_of_Fatigue_Crack_Propagation_Considering_Nonlinear_Damage_Accumulation/fulltext/577e9e2708ae9485a436874b/An-Artificial-Neural-Network-Based-Algorithm-for-Evaluation-of-Fatigue-Crack-Propaga> [retrieved on 20210218], DOI: 10.3390/ma9060483 *

Also Published As

Publication number Publication date
CN110431395A (en) 2019-11-08
WO2018169722A1 (en) 2018-09-20
EP3596439A1 (en) 2020-01-22

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