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Reliability analysis of the main drive system of a CNC machine tool including early failures

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  • Li, He
  • Deng, Zhi-Ming
  • Golilarz, Noorbakhsh Amiri
  • Guedes Soares, C.

Abstract

Early failures occur in the initial operation period of complicated systems such as Main Drive Systems (MDSs) of Computerized Numerical Control machine tools (CNC machine tools). In this paper, a Bayesian network model is developed to conduct a comprehensive reliability analysis of the MDS of a heavy boring and milling CNC machine tool, in which early failures of the MDS is considered. The primary contributions of this study over the existing analyses are: (i) the early failure of the MDS is investigated. (ii) the reliability analysis is conducted under the complicated system assumption, accordingly, multiple working states of the MDS and its subsystems are considered that are working, have a soft failure or a hard failure. (iii) reliability of the MDS in different working stages and for various manufacturing tasks are analysed. With the Bayesian network model, reliability and mean time to failure of the MDS and its subsystems are predicted. Meanwhile, this study identified risky failure items that potentially give rise to malfunctions of the MDS. The error of the predicted results is 8% at the early-wear stage and 10.5% at the stable-working stage when comparison with collected field data. Recommendations on improvements of maintenance and inspection activities are suggested, which may play a role in overall cost saving and guarantee the reliability of the MDS of the heavy boring and milling CNC machine tool.

Suggested Citation

  • Li, He & Deng, Zhi-Ming & Golilarz, Noorbakhsh Amiri & Guedes Soares, C., 2021. "Reliability analysis of the main drive system of a CNC machine tool including early failures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003665
    DOI: 10.1016/j.ress.2021.107846
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    Cited by:

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    2. Ma, Chenyang & Li, Yongbo & Wang, Xianzhi & Cai, Zhiqiang, 2023. "Early fault diagnosis of rotating machinery based on composite zoom permutation entropy," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Raman Kumar & Shubham Sharma & Ranvijay Kumar & Sanjeev Verma & Mohammad Rafighi, 2023. "Review of Lubrication and Cooling in Computer Numerical Control (CNC) Machine Tools: A Content and Visualization Analysis, Research Hotspots and Gaps," Sustainability, MDPI, vol. 15(6), pages 1-44, March.

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