Skip to main content
Log in

An early fault elimination method of computerized numerical control machine tools

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Early faults of product have plagued many machine tool companies, especially middle-small enterprises limited by capital. To eliminate the early faults and improve the reliability of computerized numerical control (CNC) machine tools, we propose a systematic early fault elimination method based on the after-sale data (customer field data) of CNC machine tools. The proposed method includes four steps: fault data collection; a four-parameter non-homogenous Poisson process (NHPP) model; a mixed fault analysis method that is the combination of fault tree analysis (FTA) method and fault mode, effects, and criticality analysis (FMECA) method; and early fault elimination measures. The first step is the basis of the analysis. The second step is used to determine the early faults from the fault data. The third step is to analyze the early faults determined. Risk priority number (RPN) is calculated to assess the criticality of the fault causes determined by the FTA. The elimination measures are taken in the end. We apply our method to a machine tool company in China, and the results prove the practicality and effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Birolini A (2014) Reliability engineering theory and practice. Springer, Heidelberg, New York Dordrecht London

    MATH  Google Scholar 

  2. Wang YQ, Jia YZ, Jiang WW (2001) Early failure analysis of machining centers: a case study. Reliab Eng Syst Saf 72:91–97

    Google Scholar 

  3. Aydin I, Karakose M, Akin E (2011) A new method for early fault detection and diagnosis of broken rotor bars. Energ Convers Manage 52(4):1790–1799

    Google Scholar 

  4. Niu XW, Zhu LM, Ding H (2005) New statistical moments for the detection of defects in rolling element bearings. Int J Adv Manuf Technol 26:1268–1274

    Google Scholar 

  5. Bangalore P, Tjernberg LB (2015) An artificial neural network approach for early fault detection of gearbox bearings. IEEE T Smart Grid 6(2):980–987

    Google Scholar 

  6. Li HK, Ren YJ, He DL, Cong M (2015) Early fault feature determination for rolling element bearing based by using improved reassigned wavelet scalogram. Instrumentation & Measurement Technology Conference, IEEE

    Google Scholar 

  7. Hu AJ, Lin JF, Sun SF, Xiang L (2017) A novel approach of impulsive signal extraction for early fault detection of rolling element bearing. Shock Vib 2017:1–11

    Google Scholar 

  8. Wan ST, Peng B (2019) Adaptive asymmetric real Laplace wavelet filtering and its application on rolling bearing early fault diagnosis. Shock Vib 2019:1–20

    Google Scholar 

  9. Huang GQ, Shi J, Mak KL (2000) Failure mode and effect analysis (FMEA) over the WWW. Int J Adv Manuf Technol 16:603–608

    Google Scholar 

  10. Chin KS, Zheng LY, Wei L (2003) A hybrid rough-cut process planning for quality. Int J Adv Manuf Technol 22:733–743

    Google Scholar 

  11. Chin KS, Chan A, Yang JB (2008) Development of a fuzzy FMEA based product design system. Int J Adv Manuf Technol 36:633–649

    Google Scholar 

  12. Gan L, Xu JP, Han BT (2012) A computer-integrated FMEA for dynamic supply chains in a flexible-based environment. Int J Adv Manuf Technol 59:697–717

    Google Scholar 

  13. Jia ZC, Yang ZJ, Shen GX (2013) Early failure mode effect and criticality analysis for CNC machining tools. Appl Mech Mater 303-306:1653–1656

    Google Scholar 

  14. Carpitella S, Certa A, Izquierdo J, Fata CML (2018) A combined multi-criteria approach to support FMECA analyses: a real-world case. Reliab Eng Syst Saf 169:394–402

    Google Scholar 

  15. Wang L, Gao Y, Xu W, Hong KX, Wang BR, Chen XA (2019) An extended FMECA method and its fuzzy assessment model for equipment maintenance management optimization. J Fail Anal Prev 2019:1–11

    Google Scholar 

  16. Goo B, Lee J, Seo S, Chang D, Chung H (2017) Design of reliability critical system using axiomatic design with FMECA. Int J Nav Arch and Ocean Eng 2017:S2092678217301966

    Google Scholar 

  17. Singh J, Singh S, Singh A (2019) Distribution transformer failure modes, effects and criticality analysis (FMECA). Eng Fail Anal 99:180–191

    Google Scholar 

  18. Li LQ (2012) Handbook of certified reliability engineer. China Renmin University Press, Beijing

    Google Scholar 

  19. Jin L, Peng C, Tao J (2017) System-level electric field exposure assessment by the fault tree analysis. IEEE T Electromagn C 59(4):1095–1102

    Google Scholar 

  20. Jetter JJ, Jr RF, Rubenstein R (2010) Fault tree analysis for exposure to refrigerants used for automotive air conditioning in the United States. Risk Anal 21(1):157–171

    Google Scholar 

  21. Kabir S, Walker M, Papadopoulos Y, Rude E, Securius P (2016) Fuzzy temporal fault tree analysis of dynamic systems. Int J Approx Reason 77(C):20–37

    MathSciNet  MATH  Google Scholar 

  22. Purba HJ (2014) A fuzzy-based reliability approach to evaluate basic events of fault tree analysis for nuclear power plant probabilistic safety assessment. Ann Nucl Energy 70:21–29

    Google Scholar 

  23. Huang W, Fan H, Qiu Y, Cheng ZY, Qian Y (2016) Application of fault tree approach for the causation mechanism of urban haze in Beijing—considering the risk events related with exhausts of coal combustion. Sci Total Environ 544:1128–1135

    Google Scholar 

  24. Ammar M, Hamad GB, Mohamed OA, Savaria Y (2019) Towards an accurate probabilistic modeling and statistical analysis of temporal faults via temporal dynamic fault-trees (TDFTs). IEEE Access 7:29264–29276

    Google Scholar 

  25. Joshi C, Ruggeri F, Wilson SP (2017) Prior robustness for bayesian implementation of the fault tree analysis. IEEE T Reliab 2017:1–14

    Google Scholar 

  26. Volk M, Junges S, Katoen JP (2018) Fast dynamic fault tree analysis by model checking techniques. IEEE T Ind Inform 14(1):370–379

    Google Scholar 

  27. Yan RD, Jackson LM, Dunnett SJ Automated guided vehicle mission reliability modelling using a combined fault tree and Petri net approach. Int J Adv Manuf Technol 92:1825–1837

    Google Scholar 

  28. Zheng Y, Zhao F, Wang Z Fault diagnosis system of bridge crane equipment based on fault tree and Bayesian network. Int J Adv Manuf Technol 105:3605–3618

    Google Scholar 

  29. Peeters JFW, Basten RJI, Tinga T (2018) Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner. Reliab Eng Syst Saf 172:36–44

    Google Scholar 

  30. Azadeh A, Sheikhalishahi M, Aghsami A (2015) An integrated FTA-DFMEA approach for reliability analysis and product configuration considering warranty cost. Prod Eng 9(5–6):635–646

    Google Scholar 

  31. Liao XB (2010) Quantitative modeling &application study of failure rate bathtub curve of machine tool. Chongqing University, Chongqing

    Google Scholar 

  32. Chen DS, Wang TM, Wei HX (2005) Sectional model involving two Weibull distributions for CNC lathe failure probability. J Beijing U Aeronaut and Astronaut 31(7):766–769 (in Chinese)

    Google Scholar 

  33. Xu BB, Yang ZJ, Chen F, Hao QB, Zhao HW, Li GF (2011) Reliability model of CNC machine tools based on non-homogenous Poisson process. J Jilin U (Eng Technol Edit) 41(2):210–214 (in Chinese)

    Google Scholar 

  34. Ao CL, Li YJ, Yan XB, Chu FF (2007) Operational reliability of tractor engines based on non-homogeneous Poisson process. Chin J Mech Eng 43(10):206–210 (in Chinese)

    Google Scholar 

  35. Cui YY, Quan CY, Ding LP, Gao HS (2000) Model for assessing operational reliability of repairable aircraft systems and its application. Acta Aeronautica Et Astronautica Sinica 21(4) (in Chinese)

  36. Pulcini G (2001) Modeling the failure data of a repairable equipment with bathtub type failure intensity. Reliab Eng Syst Saf 71(2):209–218

    Google Scholar 

  37. Jiang R (2013) A new bathtub curve model with a finite support. Reliab Eng Syst Saf 119:44–51

    Google Scholar 

  38. He GF, Qu RZ (1995) Collection and analysis of reliability dat. National defense industry press, Beijing

    Google Scholar 

  39. Zhang XG (2016) Research on reliability data related technology of CNC machine tools. Chongqing University, Chongqing

    Google Scholar 

  40. Wei LH (2011) Coupling modeling and influence analysis of availability of numerical control machine tools. Jinlin University, Changchun, Jilin

    Google Scholar 

  41. Ren LN, Rui ZY, JH LI (2014) Reliability analysis of numerical control machine tools with bounded and bathtub shaped failure intensity. Chin J Mech Eng 50(16):13–20 (in Chinese)

    Google Scholar 

  42. Zhang GB, Zhang KN, Wang Y, Kang LN (2016) Research on intensity function bathtub curve model for multiple CNC machine tools. Mech Sci and Technol Aerosp Eng 35(1):104–108 (in Chinese)

    Google Scholar 

  43. Louit DM, Pascual R, Jardine AKS (2009) A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data. Reliab Eng Syst Saf 94(10):1618–1628

    Google Scholar 

  44. Zhang ZZ (2010) Reliability-centered quality design, analysis and control. Publishing house of electronics industry. Beijing, China

    Google Scholar 

  45. Xiao NC, Huang HZ, Li YF, He L, Jin TD (2011) Multiple failure modes analysis and weighted risk priority number evaluation in FMEA. Eng Fail Anal 18(4):1162–1170

    Google Scholar 

  46. Wang YM, Chin KS, Poon GKK, Yang JB (2009) Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert Syst Appl 36(2-part-P1):1195–1207

    Google Scholar 

  47. GB/T 23567.1–2009 Reliability evaluation for numerical control machine tools-Part 1: General rule

  48. Shu JS, Guo BB, Zhang JY, Zhang ZR (2008) Research on probability distribution of parameters of rock and soil based on fitting optimization index. J Min Safe Eng 2(25):197–201 (in Chinese)

    Google Scholar 

  49. Wang YY, Li WF (2017) An anti-overturning mechanism for tailstock of CNC machine tool. Mech Eng 7:123–123 (in Chinese)

    Google Scholar 

Download references

Funding

An earlier partial version of this paper was presented at the 2018 International Conference on Intelligent Manufacturing and Internet of Things (IMIOT 2018) and International Conference on Intelligent Computing for Sustainable Energy and Environment (ICSEE 2018) in September 21–23 in Chongqing, China. We specifically thank our cooperative corporation (Baoji Machine Tool Group Co., Ltd. in China) for giving the support of the research. This work is sponsored in part by the National Natural Science Foundation of China under Grants 51835001 and 51705048, in part by the National Major Scientific and Technological Special Project for “High-grade CNC and Basic Manufacturing Equipment”, China under Grant 2018ZX04032-001.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yan Ran.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Li, Y., Zhang, G. et al. An early fault elimination method of computerized numerical control machine tools. Int J Adv Manuf Technol 106, 5049–5059 (2020). https://doi.org/10.1007/s00170-020-04956-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-04956-0

Keywords

Navigation