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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
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    References listed on IDEAS

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