IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i7d10.1007_s10845-024-02461-9.html

CNC linear axis condition-based monitoring: a statistics-based framework to establish a baseline dataset and case study

Author

Listed:
  • Andres Hurtado Carreon

    (McMaster Manufacturing Research Institute (MMRI), McMaster University)

  • Jose Mario DePaiva

    (McMaster Manufacturing Research Institute (MMRI), McMaster University)

  • Rohan Barooah

    (McMaster Manufacturing Research Institute (MMRI), McMaster University)

  • Stephen C. Veldhuis

    (McMaster Manufacturing Research Institute (MMRI), McMaster University)

Abstract

The linear axis of computer numerical control (CNC) machines is a critical subsystem that provides precise position capabilities. The unexpected failure of its components may lead to part quality issues and machine breakdowns. Therefore, it is crucial to examine and understand its healthy condition when newly commissioned or repaired so that it can be used as a reference when monitoring its operational health. In this paper, a framework to establish a baseline reference dataset is proposed utilizing vibration monitoring and time domain statistical feature analysis. The framework was applied as a case study in a newly commissioned linear axis testbed. The results demonstrated that a linear axis under a known healthy condition exhibits low variability of its time domain features, negligible difference between forward and reverse stroke directions and a robust baseline dataset can be established by collecting data for approximately an hour of operation instead of a full day of operation (6 h of operation).

Suggested Citation

  • Andres Hurtado Carreon & Jose Mario DePaiva & Rohan Barooah & Stephen C. Veldhuis, 2025. "CNC linear axis condition-based monitoring: a statistics-based framework to establish a baseline dataset and case study," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4613-4634, October.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02461-9
    DOI: 10.1007/s10845-024-02461-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02461-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-024-02461-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
    2. Rosmaini Ahmad & Shahrul Kamaruddin, 2012. "A review of condition-based maintenance decision-making," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 6(5), pages 519-541.
    3. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    4. Xiao Liu & Jingrui Li & Khalifa Al-Khalifa & Abdelmagid Hamouda & David Coit & Elsayed Elsayed, 2013. "Condition-based maintenance for continuously monitored degrading systems with multiple failure modes," IISE Transactions, Taylor & Francis Journals, vol. 45(4), pages 422-435.
    5. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    6. Yang Hui & Xuesong Mei & Gedong Jiang & Fei Zhao & Ziwei Ma & Tao Tao, 2022. "Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 753-769, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huynh, K.T. & Grall, A. & Bérenguer, C., 2017. "Assessment of diagnostic and prognostic condition indices for efficient and robust maintenance decision-making of systems subject to stress corrosion cracking," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 237-254.
    2. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    3. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    4. Hai-Kun Wang & Yan-Feng Li & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Remaining useful life estimation under degradation and shock damage," Journal of Risk and Reliability, , vol. 229(3), pages 200-208, June.
    5. Abu MD Ariful Islam & Jørn Vatn, 2023. "Condition-based multi-component maintenance decision support under degradation uncertainties," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 961-979, December.
    6. Ye, Zhi-Sheng & Chen, Nan & Shen, Yan, 2015. "A new class of Wiener process models for degradation analysis," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 58-67.
    7. Patrick Zschech & Kai Heinrich & Raphael Bink & Janis S. Neufeld, 2019. "Prognostic Model Development with Missing Labels," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 327-343, June.
    8. Hu, Yang & Baraldi, Piero & Di Maio, Francesco & Zio, Enrico, 2015. "A particle filtering and kernel smoothing-based approach for new design component prognostics," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 19-31.
    9. Gilberto Santos & Jose Carlos Sá & Maria João Félix & Luís Barreto & Filipe Carvalho & Manuel Doiro & Kristína Zgodavová & Miladin Stefanović, 2021. "New Needed Quality Management Skills for Quality Managers 4.0," Sustainability, MDPI, vol. 13(11), pages 1-22, May.
    10. Verônica Maurer Tabim & Néstor Fabián Ayala & Alejandro G. Frank, 2024. "Implementing Vertical Integration in the Industry 4.0 Journey: Which Factors Influence the Process of Information Systems Adoption?," Information Systems Frontiers, Springer, vol. 26(5), pages 1615-1632, October.
    11. Qiao, Penghua & Qiu, Kaizhong & Fung, Anna & Yau, Jot & Fung, Hung-Gay, 2025. "Exploring the linkage between smart manufacturing technology and outward foreign direct investment in the new digital information age," Research in International Business and Finance, Elsevier, vol. 79(C).
    12. Michal Gluszak & Remigiusz Gawlik & Malgorzata Zieba, 2019. "Smart and Green Buildings Features in the Decision-Making Hierarchy of Office Space Tenants: An Analytic Hierarchy Process Study," Administrative Sciences, MDPI, vol. 9(3), pages 1-16, July.
    13. Zhang, Jian-Xun & Hu, Chang-Hua & He, Xiao & Si, Xiao-Sheng & Liu, Yang & Zhou, Dong-Hua, 2017. "Lifetime prognostics for deteriorating systems with time-varying random jumps," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 338-350.
    14. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    15. KarabaÄŸ, Oktay & Eruguz, Ayse Sena & Basten, Rob, 2020. "Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    16. Zhengxin Zhang & Xiaosheng Si & Changhua Hu & Xiangyu Kong, 2015. "Degradation modeling–based remaining useful life estimation: A review on approaches for systems with heterogeneity," Journal of Risk and Reliability, , vol. 229(4), pages 343-355, August.
    17. Tortorella, Guilherme Luz & Narayanamurthy, Gopalakrishnan & Thurer, Matthias, 2021. "Identifying pathways to a high-performing lean automation implementation: An empirical study in the manufacturing industry," International Journal of Production Economics, Elsevier, vol. 231(C).
    18. Ismael Cristofer Baierle & Francisco Tardelli da Silva & Ricardo Gonçalves de Faria Correa & Jones Luís Schaefer & Matheus Becker Da Costa & Guilherme Brittes Benitez & Elpidio Oscar Benitez Nara, 2022. "Competitiveness of Food Industry in the Era of Digital Transformation towards Agriculture 4.0," Sustainability, MDPI, vol. 14(18), pages 1-22, September.
    19. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    20. Gupta, Nitin & Misra, Neeraj & Kumar, Somesh, 2015. "Stochastic comparisons of residual lifetimes and inactivity times of coherent systems with dependent identically distributed components," European Journal of Operational Research, Elsevier, vol. 240(2), pages 425-430.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02461-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.