Advertisement

Reliability assessment for CNC equipment based on degradation data

  • Chaoqun Duan
  • Chao DengEmail author
  • Ning Li
ORIGINAL ARTICLE
  • 43 Downloads

Abstract

The computer numerical control (CNC) equipment is playing essential role in production and manufacturing, thus, its reliability estimation is crucial for industrial systems. Existing research studies mainly focused on univariate degradation data or multiple correlated data, which might not be accurate in equipment reliability assessment. In real CNC equipment, the degradation data consists of correlated and non-correlated degradation data and the data sample might be scarce, which bring great challenges for maintenance managers to assess risk and prevent failure ahead of fault occurrence. This paper proposes a reliability assessment approach based on multiple non-correlated and correlated degradation data with focusing on small size of data sample. Using the multiple degradation data of CNC equipment, a covariance matrix is built to determine the correlation between the performance signals. If the performance signals are not correlated, the degradation trajectory of the performance signals is curve fitted by least squares support vector machines (LS-SVM). The reliability which indicates the degradation curve not reaching its specified threshold is calculated. If the performance signals are correlated, according to the degradation data, the degradation curve of the performance signal is fitted by multivariate regression using LS-SVM, and the joint probability density function is derived. According to both the reliabilities of non-correlated degradation data and correlated degradation data, the joint reliability of the system is calculated. Finally, using a specific type of CNC machine tool as an example, the performance reliability assessment method is verified.

Keywords

CNC equipment Reliability assessment Degradation data Condition monitoring Least squares support vector machines (LS-SVM) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

The authors are grateful to the technical editor and all reviewers for their valuable and constructive comments.

Funding information

The research was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 51605095; the National Key Research and Development Program of China under Grant No. 2016YFE0121700; and the Science and Technology Development Fund of Macao S.A.R. (FDCT) under MoST-FDCT Joint Grant No. 015/2015/AMJ.

References

  1. 1.
    Keizer MCO, Flapper SDP, Teunter RH (2017) Condition-based maintenance policies for systems with multiple dependent components: a review. Eur J Oper Res 461(2):405–420MathSciNetzbMATHGoogle Scholar
  2. 2.
    Keller AZ, Kamath ARR, Perera UD (1982) Reliability analysis of CNC machine tools. Reliab Eng 3(6):449–473Google Scholar
  3. 3.
    Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D (2014) Prognostics and health management design for rotary machinery systems—reviews, methodology and applications. Mech Syst Signal Process 42(1):314–334Google Scholar
  4. 4.
    Byington CS, Watson MJ, Sheldon JS, Swerdon GM (2009) Shaft coupling model-based prognostics enhanced by vibration diagnostics. Or Insight 51(8):420–425Google Scholar
  5. 5.
    Orsagh R, Roemer M, Sheldon J, Klenke CJ (2004) A comprehensive prognostics approach for predicting gas turbine engine bearing life. In proceedings of the ASME Turbo expo (Vol 2, pp 777–785)Google Scholar
  6. 6.
    Muir D, Taylor B (1997) Oil debris monitoring for aeroderivative gas turbine. ASME Power Divi (Publ) PWR 32:547–553Google Scholar
  7. 7.
    Roemer MJ, Kacprzynski GJ, Orsagh RF (2001) Assessment of data and knowledge fusion strategies for prognostics and health management. In aerospace conference, IEEE proceedings. (Vol 6, pp 2979–2988) IEEEGoogle Scholar
  8. 8.
    Hansen RJ, Hall DL, Kurtz SK (1994) A new approach to the challenge of machinery prognostics. In ASME 1994 international gas turbine and Aeroengine congress and exposition. American Society of Mechanical Engineers, pp V005T15A001–V005T15A001Google Scholar
  9. 9.
    Reichard KM, Van Dyke M, Maynard K (2000) Application of sensor fusion and signal classification techniques in a distributed machinery condition monitoring system. In Proc SPIE Int Soc Opt Eng, Vol 4051, pp 329–336Google Scholar
  10. 10.
    Crow EC, Reichard K, Rogan C, Callen J, Seifert E, Street NA (2007) Integrated multi-sensor package (IMSP) for unmanned vehicle operations. Unmanned/unattended sensors and sensor networks, pp 673604–673612Google Scholar
  11. 11.
    Tan CK, Irving P, Mba D (2007) A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears. Mech Syst Signal Process 21(1):208–233Google Scholar
  12. 12.
    Liu Q, Dong M, Lv W, Geng X, Li Y (2015) A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis. Mech Syst Signal Process 64:217–232Google Scholar
  13. 13.
    Duan C, Deng C, Gong Q, Wang Y (2018) Optimal failure mode-based preventive maintenance scheduling for complex mechanical device. Int J Adv Manuf Technol 95(5–8):2717–2728Google Scholar
  14. 14.
    Luo J, Pattipati KR, Qiao L, Chigusa S (2008) Model-based prognostic techniques applied to a suspension system. IEEE T SYST MAN CY A 38(5):1156–1168Google Scholar
  15. 15.
    Si XS, Wang W, Hu CH, Zhou DH (2011) Remaining useful life estimation–a review on the statistical data driven approaches. Eur J Oper Res 213(1):1–14MathSciNetGoogle Scholar
  16. 16.
    Shafiee M, Finkelstein M (2015) An optimal age-based group maintenance policy for multi-unit degrading systems. Reliab Eng Syst Saf 134:230–238Google Scholar
  17. 17.
    Duan C, Deng C, Wang B (2017) Optimal multi-level condition-based maintenance policy for multi-unit systems under economic dependence. Int J Adv Manuf Technol 91(9):4299–4312Google Scholar
  18. 18.
    Hu CH, Lee MY, Tang J (2015) Optimum step-stress accelerated degradation test for wiener degradation process under constraints. Eur J Oper Res 241(2):412–421MathSciNetzbMATHGoogle Scholar
  19. 19.
    Chen N, Ye ZS, Xiang Y, Zhang L (2015) Condition-based maintenance using the inverse Gaussian degradation model. Eur J Oper Res 243(1):190–199MathSciNetzbMATHGoogle Scholar
  20. 20.
    Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510Google Scholar
  21. 21.
    Alaswad S, Xiang Y (2017) A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliab Eng Syst Saf 157:54–63Google Scholar
  22. 22.
    Duan C, Deng C, Wang B (2017) Multi-phase sequential preventive maintenance scheduling for deteriorating repairable systems. J Intell Manuf 1–15.  https://doi.org/10.1007/s10845-017-1353-z
  23. 23.
    Pulcini G (2001) An exponential reliability-growth model in multicopy testing program. IEEE Trans Reliab 50(4):365–373MathSciNetGoogle Scholar
  24. 24.
    Hurtado JE, Alvarez DA (2003) Classification approach for reliability analysis with stochastic finite-element modeling. J Struct Eng 129(8):1141–1149Google Scholar
  25. 25.
    Gui J, Kang H (2004) The study of the whole response surface method for structural reliability analysis. J Buil Struct 25(4):100–105Google Scholar
  26. 26.
    Hamada M, Martz HF, Reese CS, Graves T, Johnson V, Wilson AG (2004) A fully Bayesian approach for combining multilevel failure information in fault tree quantification and optimal follow-on resource allocation. Reliab Eng Syst Saf 86(3):297–305Google Scholar
  27. 27.
    Quigley J, Walls L (2005) Nonparametric bootstrapping of the reliability function for multiple copies of a repairable item modeled by a birth process. IEEE Trans Reliab 54(4):604–611Google Scholar
  28. 28.
    Do Van P, Barros A, Bérenguer C (2008) Reliability importance analysis of Markovian systems at steady state using perturbation analysis. Reliab Eng Syst Saf 93(11):1605–1615Google Scholar
  29. 29.
    Guo H, Yang X (2008) Automatic creation of Markov models for reliability assessment of safety instrumented systems. Reliab Eng Syst Saf 93(6):829–837Google Scholar
  30. 30.
    Cardoso JB, de Almeida JR, Dias JM, Coelho PG (2008) Structural reliability analysis using Monte Carlo simulation and neural networks. Adv Eng Softw 39(6):505–513Google Scholar
  31. 31.
    Vapnik V (2013) The nature of statistical learning theory. Springer Science & Business Media, BerlinzbMATHGoogle Scholar
  32. 32.
    Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300Google Scholar
  33. 33.
    Clarke SM, Griebsch JH, Simpson TW (2005) Analysis of support vector regression for approximation of complex engineering analyses. J Mech Des 127(6):1077–1087Google Scholar
  34. 34.
    Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KR (2000) A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Trans Neural Netw 11(1):124–136Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.School of Mechatronics Engineering and Automation Shanghai UniversityShanghaiPeople’s Republic of China

Personalised recommendations