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      Methodology for the integration of a high-speed train in Maintenance 4.0

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      https://www.riss.kr/link?id=A107954590

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      다국어 초록 (Multilingual Abstract)

      The fourth industrial revolution is changing the way industries face their problems, including maintenance. The railway industry is moving to adopt this new industry model. The new trains are designed, manufactured, and maintained following an Industr...

      The fourth industrial revolution is changing the way industries face their problems, including maintenance. The railway industry is moving to adopt this new industry model. The new trains are designed, manufactured, and maintained following an Industry 4.0 methodology, but most of the current trains in operation were not designed with this technological philosophy, so they must be adapted to it. In this paper, a new methodology for adapting a high-speed train to Industry 4.0 is proposed. That way, a train manufactured before this new paradigm can seize the advantages of Maintenance 4.0. This methodology is based on four stages (physical system, digital twin, information and communication technology infrastructure, and diagnosis) that comprise the required processes to digitalize a railway vehicle and that share information between them. The characteristics that the data acquisition and communication systems must fulfil are described, as well as the original signal processing techniques developed for analysing vibration signals. These techniques allow processing experimental data both in real time and deferred, according to actual maintenance requirements. The methodology is applied to determine the operating condition of a high-speed bogie by combining the signal processing of actual vibration measurements taken during the normal train operation and the data obtained from simulations of the digital twin. The combination of both (experimental data and simulations) allows establishing characteristic indicators that correspond to the normal running of the train and indicators that would correspond to anomalies in the behaviour of the train.

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      참고문헌 (Reference)

      1 Arash Amini, "Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high-frequency acoustic emission signals" SAGE Publications 8 (8): 168781401667600-, 2016

      2 Fortea, P., "Using predictive maintenance to improve safety and efficiency of railways"

      3 Grieves, M., "Transdisciplinary perspectives on complex systems" Springer International Publishing 85-113, 2017

      4 Huang, N. E., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis" 454 : 903-995, 1998

      5 Heletjé E. van Staden, "The effect of multi-sensor data on condition-based maintenance policies" Elsevier BV 290 (290): 585-600, 2021

      6 Yung-Hsiang Cheng, "Rolling stock maintenance strategy selection, spares parts’ estimation, and replacements’ interval calculation" Elsevier BV 128 (128): 404-412, 2010

      7 Zhang Yun, "Reverse modeling strategy of aero-engine blade based on design intent" Springer Science and Business Media LLC 81 (81): 1781-1796, 2015

      8 Won-Hyuk Lee, "Registration method for maintenance-work support based on augmented-reality-model generation from drawing data" 한국CDE학회 7 (7): 775-787, 2020

      9 Thompson, D., "Railway noise and vibration" Elsevier 2009

      10 María Jesús Gómez, "Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines" MDPI AG 20 (20): 3575-, 2020

      1 Arash Amini, "Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high-frequency acoustic emission signals" SAGE Publications 8 (8): 168781401667600-, 2016

      2 Fortea, P., "Using predictive maintenance to improve safety and efficiency of railways"

      3 Grieves, M., "Transdisciplinary perspectives on complex systems" Springer International Publishing 85-113, 2017

      4 Huang, N. E., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis" 454 : 903-995, 1998

      5 Heletjé E. van Staden, "The effect of multi-sensor data on condition-based maintenance policies" Elsevier BV 290 (290): 585-600, 2021

      6 Yung-Hsiang Cheng, "Rolling stock maintenance strategy selection, spares parts’ estimation, and replacements’ interval calculation" Elsevier BV 128 (128): 404-412, 2010

      7 Zhang Yun, "Reverse modeling strategy of aero-engine blade based on design intent" Springer Science and Business Media LLC 81 (81): 1781-1796, 2015

      8 Won-Hyuk Lee, "Registration method for maintenance-work support based on augmented-reality-model generation from drawing data" 한국CDE학회 7 (7): 775-787, 2020

      9 Thompson, D., "Railway noise and vibration" Elsevier 2009

      10 María Jesús Gómez, "Railway Axle Condition Monitoring Technique Based on Wavelet Packet Transform Features and Support Vector Machines" MDPI AG 20 (20): 3575-, 2020

      11 Thompson, C., "Predictive maintenance approaches based on continuous monitoring systems at Rio Tinto" 7-, 2016

      12 Mayorkinos Papaelias, "Online condition monitoring of rolling stock wheels and axle bearings" SAGE Publications 230 (230): 709-723, 2016

      13 Esteban Bernal, "Onboard Condition Monitoring Sensors, Systems and Techniques for Freight Railway Vehicles: A Review" Institute of Electrical and Electronics Engineers (IEEE) 19 (19): 4-24, 2019

      14 Jong-Ho Shin, "On condition based maintenance policy" Oxford University Press (OUP) 2 (2): 119-127, 2015

      15 Zougari, A., "Numerical models of railway tracks for obtaining frequency response comparisonwith analytical results and experimental measurements" 18 (18): 11-, 2016

      16 Ngigi, R. W., "Modern techniques for condition monitoring of railway vehicle dynamics" 364 : 012016-, 2012

      17 Alessandro Ceruti, "Maintenance in aeronautics in an Industry 4.0 context: The role of Augmented Reality and Additive Manufacturing" 한국CDE학회 6 (6): 516-526, 2019

      18 Kans, M., "Maintenance 4.0 in railway transportation industry" Springer International Publishing 317-331, 2016

      19 Takikawa, M., "Innovation in railway maintenance utilizing information and communication technology(smart maintenance initiative)" 67 : 14-, 2016

      20 Heiner Lasi, "Industry 4.0" Springer Science and Business Media LLC 6 (6): 239-242, 2014

      21 "In-Depth Focus: Digital Twins" 27 (27): 19-, 2021

      22 N. Geren, "Improvement of a low-cost water jet machining intensifier using reverse engineering and redesign methodology" Informa UK Limited 18 (18): 13-37, 2007

      23 Li, Z., "Identification method of wheel flat based on Hilbert–Huang transform" 12 (12): 33-41, 2012

      24 David Lebel, "High-speed train suspension health monitoring using computational dynamics and acceleration measurements" Informa UK Limited 58 (58): 911-932, 2020

      25 Yinling Ke, "Feature-based reverse modeling strategies" Elsevier BV 38 (38): 485-506, 2006

      26 Yifan Li, "Fault detection method for railway wheel flat using an adaptive multiscale morphological filter" Elsevier BV 84 : 642-658, 2017

      27 Liyuan Su, "Fault Diagnosis of High-Speed Train Bogie by Residual-Squeeze Net" Institute of Electrical and Electronics Engineers (IEEE) 15 (15): 3856-3863, 2019

      28 Mohamed Hassan, "Experimental and numerical investigation of the possibilities for the structural health monitoring of railway axles based on acceleration measurements" SAGE Publications 18 (18): 902-919, 2019

      29 Alejandro Bustos, "Enhancement of chromatographic spectral technique applied to a high‐speed train" Wiley 28 (28): e2842-, 2021

      30 Yun Zhang, "Efficient measurement of aero-engine blade considering uncertainties in adaptive machining" Springer Science and Business Media LLC 86 (86): 387-396, 2016

      31 Alejandro Bustos, "EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State" MDPI AG 18 (18): 793-, 2018

      32 Miguel Angel Navas, "Disruptive Maintenance Engineering 4.0" Emerald 37 (37): 853-871, 2020

      33 Lai, C. C., "Development of a fiber-optic sensing system for train vibration and train weight measurements in Hong Kong" 2012 : 1-7, 2012

      34 Paul Hyde, "Development and testing of an automatic remote condition monitoring system for train wheels" Institution of Engineering and Technology (IET) 10 (10): 32-40, 2016

      35 Zasiadko, M., "Deutsche Bahn produces heavy spare parts on 3D printer"

      36 Alejandro Bustos, "Condition monitoring of critical mechanical elements through Graphical Representation of State Configurations and Chromogram of Bands of Frequency" Elsevier BV 135 : 71-82, 2019

      37 Alireza Alemi, "Condition monitoring approaches for the detection of railway wheel defects" SAGE Publications 231 (231): 961-981, 2017

      38 Fiona Zhao, "Computer-Aided Inspection Planning—The state of the art" Elsevier BV 60 (60): 453-466, 2009

      39 Mehmet Karakose, "Complex Fuzzy System Based Predictive Maintenance Approach in Railways" Institute of Electrical and Electronics Engineers (IEEE) 16 (16): 6023-6032, 2020

      40 Chunsheng Li, "Bolster spring fault detection strategy for heavy haul wagons" Informa UK Limited 56 (56): 1604-1621, 2018

      41 Gabriel Rilling, "Bivariate Empirical Mode Decomposition" Institute of Electrical and Electronics Engineers (IEEE) 14 (14): 936-939, 2007

      42 Radhya Sahal, "Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case" Elsevier BV 54 : 138-151, 2020

      43 D.P. Connolly, "Benchmarking railway vibrations – Track, vehicle, ground and building effects" Elsevier BV 92 : 64-81, 2015

      44 Palmgren, A., "Ball and roller bearing engineering" SKF Industries Inc 1959

      45 Rob S Dwyer-Joyce, "An ultrasonic sensor for monitoring wheel flange/rail gauge corner contact" SAGE Publications 227 (227): 188-195, 2013

      46 Chunsheng Li, "An overview: modern techniques for railway vehicle on-board health monitoring systems" Informa UK Limited 55 (55): 1045-1070, 2017

      47 Alstom, "Alstom launches HealthHub, an innovative tool for predictive maintenance"

      48 Saidy, C., "Advances in asset management and condition monitoring(Vol. 166)" Springer International Publishing 1039-1049, 2020

      49 Xie, X., "A review of recent advances in surface defect detection using texture analysis techniques" 7 (7): 1-, 2008

      50 See Yenn Chong, "A review of health and operation monitoring technologies for trains" 국제구조공학회 6 (6): 1079-1105, 2010

      51 Medeiros, L., "A prototype for monitoring railway vehicle dynamics using inertial measurement units" 149-154, 2018

      52 R. Jill Urbanic, "A design recovery framework for mechanical components" Informa UK Limited 20 (20): 195-215, 2009

      53 R. J. Urbanic, "A design and inspection based methodology for form-function reverse engineering of mechanical components" Springer Science and Business Media LLC 81 (81): 1539-1562, 2015

      54 George Lederman, "A data fusion approach for track monitoring from multiple in-service trains" Elsevier BV 95 : 363-379, 2017

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2017-03-13 학술지명변경 한글명 : Journal of Computational Design and Engineering -> Journal of Computational Design and Engineering
      외국어명 : Journal of Computational Design and Engineering -> Journal of Computational Design and Engineering
      KCI등재
      2017-03-01 평가 SCOPUS 등재 (기타) KCI등재
      2016-06-13 학회명변경 한글명 : 한국CAD/CAM학회 -> 한국CDE학회
      영문명 : Society Of Cadcam Engineers -> Society for Computational Design and Engineering
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      2016 0 0 0
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