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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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    References listed on IDEAS

    as
    1. Peng, Weiwen & Li, Yan-Feng & Mi, Jinhua & Yu, Le & Huang, Hong-Zhong, 2016. "Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 75-87.
    2. Eryilmaz, Serkan & Kan, Cihangir, 2020. "Reliability based modeling and analysis for a wind power system integrated by two wind farms considering wind speed dependence," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Li, He & Teixeira, Angelo P. & Guedes Soares, C., 2020. "A two-stage Failure Mode and Effect Analysis of offshore wind turbines," Renewable Energy, Elsevier, vol. 162(C), pages 1438-1461.
    4. Lee, Dooyoul & Choi, Dongsu, 2020. "Analysis of the reliability of a starter-generator using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Dinis, D. & Teixeira, A.P. & Guedes Soares, C., 2020. "Probabilistic approach for characterising the static risk of ships using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    6. Jinhua Mi & Yan-Feng Li & Weiwen Peng & Hong-Zhong Huang, 2018. "Reliability Analysis of Complex Multi-state System with Common Cause Failure Based on DS Evidence Theory and Bayesian Network," Springer Series in Reliability Engineering, in: Anatoly Lisnianski & Ilia Frenkel & Alex Karagrigoriou (ed.), Recent Advances in Multi-state Systems Reliability, pages 19-38, Springer.
    7. Dundulis, Gintautas & ŽutautaitÄ—, Inga & Janulionis, Remigijus & UÅ¡puras, Eugenijus & RimkeviÄ ius, Sigitas & Eid, Mohamed, 2016. "Integrated failure probability estimation based on structural integrity analysis and failure data: Natural gas pipeline case," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 195-202.
    8. Fam, Mei Ling & He, Xuhong & Konovessis, Dimitrios & Ong, Lin Seng, 2020. "Using Dynamic Bayesian Belief Network for analysing well decommissioning failures and long-term monitoring of decommissioned wells," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    9. HOSSAIN, Niamat Ullah Ibne & Amrani, Safae El & Jaradat, Raed & Marufuzzaman, Mohammad & Buchanan, Randy & Rinaudo, Christina & Hamilton, Michael, 2020. "Modeling and assessing interdependencies between critical infrastructures using Bayesian network: A case study of inland waterway port and surrounding supply chain network," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    10. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    11. Jensen, H.A. & Jerez, D.J., 2019. "A Bayesian model updating approach for detection-related problems in water distribution networks," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 100-112.
    12. Lo, Huai-Wei & Liou, James J.H. & Huang, Chun-Nen & Chuang, Yen-Ching, 2019. "A novel failure mode and effect analysis model for machine tool risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 173-183.
    13. Bing Wu & Huibin Tian & Xinping Yan & C. Guedes Soares, 2020. "A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze River using Bayesian Networks," Journal of Risk and Reliability, , vol. 234(2), pages 422-436, April.
    14. Kammouh, Omar & Gardoni, Paolo & Cimellaro, Gian Paolo, 2020. "Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    15. Li, He & Diaz, H. & Guedes Soares, C., 2021. "A developed failure mode and effect analysis for floating offshore wind turbine support structures," Renewable Energy, Elsevier, vol. 164(C), pages 133-145.
    16. Vineyard, Michael & Amoako-Gyampah, Kwasi & Meredith, Jack R., 1999. "Failure rate distributions for flexible manufacturing systems: An empirical study," European Journal of Operational Research, Elsevier, vol. 116(1), pages 139-155, July.
    17. Peng, R. & Levitin, G. & Xie, M. & Ng, S.H., 2010. "Defending simple series and parallel systems with imperfect false targets," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 679-688.
    18. Mi, Jinhua & Li, Yan-Feng & Peng, Weiwen & Huang, Hong-Zhong, 2018. "Reliability analysis of complex multi-state system with common cause failure based on evidential networks," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 71-81.
    19. Oh, ChoHwan & Lee, Jeong Ik, 2020. "Real time nuclear power plant operating state cognitive algorithm development using dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    20. Jinfen Zhang & Ângelo P Teixeira & C. Guedes Soares & Xinping Yan & Kezhong Liu, 2016. "Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks," Risk Analysis, John Wiley & Sons, vol. 36(6), pages 1171-1187, June.
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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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