Skip to main content

Advertisement

SpringerLink
Log in

Research on a configurable method for fault diagnosis knowledge of machine tools and its application

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

Abstract

Fault diagnosis is critical in PHM (Prognostics and Health Management) of machine tools due to its great significance in such efforts as prolonging lifespan, improving production efficiency, and reducing production costs. For machine tool manufacturers, a general fault diagnosis method and a software framework are needed to construct fault diagnosis systems for various machine tools and fault types, or the same type of machine tools under various life cycles, working conditions, and operating environments. A configurable method for fault diagnosis knowledge of machine tools (CMFDK-MT) is thus proposed in this paper. Firstly, an ontology-based fault diagnosis method for machine tools and an improved process of fault diagnosis with knowledge bases are introduced. Based on these, a framework of a knowledge-based configurable fault diagnosis platform for machine tools (KCFDP-MT) is designed. KCFDP-MT supports explicit knowledge representation with formal semantics, efficient knowledge utilization, and efficient integration of various fault diagnosis methods and technologies. Then, the configuration approaches for fault diagnosis activities, namely fault detection, identification, diagnosis, and solving, are studied respectively. The configuration and implementation methods of the KCFDP-MT framework are also presented. Finally, a prototype system is constructed for a CNC hobbing machine tool. Two cases of rolling bearing and gear based on signal processing are carried out to verify the effectiveness of the proposed method.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Wang Y, Deng C, Wu J, Xiong Y (2015) Failure time prediction for mechanical device based on the degradation sequence. J Intell Manuf 26(6):1181–1199. https://doi.org/10.1007/s10845-013-0849-4

    Article  Google Scholar 

  2. Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Technol 50(1–4):297–313. https://doi.org/10.1007/s00170-009-2482-0

    Article  Google Scholar 

  3. Goh KM, Tjahjono B, Baines T, Subramaniam S (2006) A review of research in manufacturing prognostics. In: 2006 4th IEEE international conference on industrial informatics. Singapore, Singapore, pp 417–422. doi:https://doi.org/10.1109/INDIN.2006.275836

  4. Muller A, Marquez AC, Iung B (2008) On the concept of e-maintenance: review and current research. Reliab Eng Syst Saf 93(8):1165–1187. https://doi.org/10.1016/j.ress.2007.08.006

    Article  Google Scholar 

  5. 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(1–2):314–334. https://doi.org/10.1016/j.ymssp.2013.06.004

    Article  Google Scholar 

  6. He X (2016) Recent development in reliability analysis of NC machine tools. Int J Adv Manuf Technol 85(1–4):115–131. https://doi.org/10.1007/s00170-015-7926-0

    Article  Google Scholar 

  7. Nurminen JK, Karonen O, Nen KHT (2003) What makes expert systems survive over 10 years—empirical evaluation of several engineering applications. Expert Syst Appl 24(2):199–211. https://doi.org/10.1016/S0957-4174(02)00149-5

    Article  Google Scholar 

  8. Liao SH (2005) Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst Appl 28(1):93–103. https://doi.org/10.1016/j.eswa.2004.08.003

    Article  Google Scholar 

  9. Wang D, Tang WH, Wu QH (2010) Ontology-based fault diagnosis for power transformers. In: 2010 IEEE Power and Energy Society General Meeting, Providence, pp 1–8. https://doi.org/10.1109/PES.2010.11845589575

  10. Lautre NK, Manna A (2006) A study on fault diagnosis and maintenance of CNC-WEDM based on binary relational analysis and expert system. Int J Adv Manuf Technol 29(5–6):490–498. https://doi.org/10.1007/BF02729101

    Article  Google Scholar 

  11. Goyal D, Pabla BS (2015) Condition based maintenance of machine tools—a review. CIRP J Manuf Sci Technol 10:24–35. https://doi.org/10.1016/j.cirpj.2015.05.004

    Article  Google Scholar 

  12. Goyal D, Pabla BS (2016) The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Arch Comput Meth Eng 23(4):585–594. https://doi.org/10.1007/s11831-015-9145-0

    Article  MathSciNet  MATH  Google Scholar 

  13. Zhang K, Yuan H, Nie P (2015) A method for tool condition monitoring based on sensor fusion. J Intell Manuf 26(5):1011–1026. https://doi.org/10.1007/s10845-015-1112-y

    Article  Google Scholar 

  14. Zargarbashi SHH, Angeles J (2015) Identification of error sources in a five-axis machine tool using FFT analysis. Int J Adv Manuf Technol 76(5–8):1353–1363. https://doi.org/10.1007/s00170-014-6323-4

    Article  Google Scholar 

  15. Krishnakumari A, Elayaperumal A, Saravanan M, Arvindan C (2017) Fault diagnostics of spur gear using decision tree and fuzzy classifier. Int J Adv Manuf Technol 89(9–12):3487–3494. https://doi.org/10.1007/s00170-016-9307-8

    Article  Google Scholar 

  16. Liu S, Hu Y, Li C, Lu H, Zhang H (2017) Machinery condition prediction based on wavelet and support vector machine. J Intell Manuf 28(4):1045–1055. https://doi.org/10.1007/s10845-015-1045-5

    Article  Google Scholar 

  17. Mosallam A, Medjaher K, Zerhouni N (2016) Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. J Intell Manuf 27(5):1037–1048. https://doi.org/10.1007/s10845-014-0933-4

    Article  Google Scholar 

  18. Zhang Z, Wang Y, Wang K (2013) Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks. Int J Adv Manuf Technol 68(1–4):763–773. https://doi.org/10.1007/s00170-013-4797-0

    Article  Google Scholar 

  19. Al-Raheem KF, Roy A, Ramachandran KP, Harrison DK, Grainger S (2009) Rolling element bearing faults diagnosis based on autocorrelation of optimized: wavelet de-noising technique. Int J Adv Manuf Technol 40(3–4):393–402. https://doi.org/10.1007/s00170-007-1330-3

    Article  Google Scholar 

  20. Lei Y, He Z, Zi Y, Hu Q (2008) Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm. Int J Adv Manuf Technol 35(9–10):968–977. https://doi.org/10.1007/s00170-006-0780-3

    Article  Google Scholar 

  21. Saravanan S, Yadava GS, Rao PV (2006) Condition monitoring studies on spindle bearing of a lathe. Int J Adv Manuf Technol 28(9–10):993–1005. https://doi.org/10.1007/s00170-004-2449-0

    Article  Google Scholar 

  22. Medina-Oliva G, Voisin A, Monnin M, Leger J (2014) Predictive diagnosis based on a fleet-wide ontology approach. Knowl-Based Syst 68:40–57. https://doi.org/10.1016/j.knosys.2013.12.020

    Article  Google Scholar 

  23. Zhou A, Yu D, Zhang W (2015) A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA. Adv Eng Inform 29(1):115–125. https://doi.org/10.1016/j.aei.2014.10.001

    Article  Google Scholar 

  24. Ebrahimipour VAYS (2015) Ontology-based knowledge platform to support equipment health in plant operations. In: Ebrahimipour V, Yacout S (eds) Ontology modeling in physical asset integrity management. Springer International Publishing, Cham, pp 221–255

    Google Scholar 

  25. Mehta P, Werner A, Mears L (2015) Condition based maintenance-systems integration and intelligence using Bayesian classification and sensor fusion. J Intell Manuf 26(2):331–346. https://doi.org/10.1007/s10845-013-0787-1

    Article  Google Scholar 

  26. Raich A, Cinar A (1994) Statistical process monitoring and disturbance isolation in multivariate continuous processes. In: Advanced control of chemical processes. Pergamon, Oxford, pp 451–456

    Google Scholar 

  27. Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220. https://doi.org/10.1006/knac.1993.1008

    Article  Google Scholar 

  28. Berners-Lee T, Hendler J (2001) Publishing on the semantic web—the coming internet revolution will profoundly affect scientific information. Nature 410(6832):1023–1024. https://doi.org/10.1038/35074206

    Article  Google Scholar 

  29. Zhou Q, Yan P, Xin Y (2017) Research on a knowledge modelling methodology for fault diagnosis of machine tools based on formal semantics. Adv Eng Inform 32:92–112. https://doi.org/10.1016/j.aei.2017.01.002

    Article  Google Scholar 

  30. Zhou Q, Yan P, Liu H, Xin Y (2017) A hybrid fault diagnosis method for mechanical components based on ontology and signal analysis. J Intell Manuf. https://doi.org/10.1007/s10845-017-1351-1

  31. W3C (2013) SPARQL 1.1 Overview. https://www.w3.org/TR/sparql11-overview/. Accessed 2016–02-25

  32. W3C (2012) OWL 2 Web Ontology Language Primer (Second Edition). https://www.w3.org/TR/2012/REC-owl2-primer-20121211/. Accessed 2017–04-19

  33. W3C (2004) SWRL: A Semantic Web Rule Language Combining OWL and RuleML. https://www.w3.org/Submission/SWRL/. Accessed 2017–04-19

  34. Zhou Q, Yan P, Liu H, Chen Y An ontology-based running status information acquisition platform and method of intelligent machine tools. Patent publication number: CN 106444631A. In Chinese

  35. Dou D, Zhou S (2016) Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery. Appl Soft Comput 46:459–468. https://doi.org/10.1016/j.asoc.2016.05.015

    Article  Google Scholar 

  36. Azadeh A, Ebrahimipour V, Bavar P (2010) A fuzzy inference system for pump failure diagnosis to improve maintenance process: the case of a petrochemical industry. Expert Syst Appl 37(1):627–639. https://doi.org/10.1016/j.eswa.2009.06.018

    Article  Google Scholar 

  37. Cai B, Liu H, Xie M (2016) A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks. Mech Syst Signal Process 80:31–44. https://doi.org/10.1016/j.ymssp.2016.04.019

    Article  Google Scholar 

  38. W3C (2017) The OWL API. https://github.com/owlcs/owlapi/wiki. Accessed 2017–05-08

  39. Saucedo-Espinosa MA, Escalante HJ, Berrones A (2017) Detection of defective embedded bearings by sound analysis: a machine learning approach. J Intell Manuf 28(2):489–500. https://doi.org/10.1007/s10845-014-1000-x

    Article  Google Scholar 

  40. Ziani R, Felkaoui A, Zegadi R (2017) Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion. J Intell Manuf 28(2):405–417. https://doi.org/10.1007/s10845-014-0987-3

    Article  Google Scholar 

  41. Cococcioni M, Lazzerini B, Volpi SL (2013) Robust diagnosis of rolling element bearings based on classification techniques. IEEE T Ind Inform 9(4):2256–2263. https://doi.org/10.1109/TII.2012.2231084

    Article  Google Scholar 

  42. Djebala A, Babouri MK, Ouelaa N (2015) Rolling bearing fault detection using a hybrid method based on empirical mode decomposition and optimized wavelet multi-resolution analysis. Int J Adv Manuf Technol 79(9–12):2093–2105. https://doi.org/10.1007/s00170-015-6984-7

    Article  Google Scholar 

  43. Smith WA, Randall RB (2015) Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech Syst Signal Process 64-65:100–131. https://doi.org/10.1016/j.ymssp.2015.04.021

    Article  Google Scholar 

  44. Xu Z, Xuan J, Shi T, Wu B, Hu Y (2009) A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique. Expert Syst Appl 36(9):11801–11807. https://doi.org/10.1016/j.eswa.2009.04.021

    Article  Google Scholar 

  45. Boutros T, Liang M (2011) Detection and diagnosis of bearing and cutting tool faults using hidden Markov models. Mech Syst Signal Process 25(6):2102–2124. https://doi.org/10.1016/j.ymssp.2011.01.013

    Article  Google Scholar 

  46. Wang C, Gan M, Zhu CA (2015) Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit. J Intell Manuf. https://doi.org/10.1007/s10845-015-1056-2

  47. Van M, Kang HJ (2016) Bearing defect classification based on individual wavelet local fisher discriminant analysis with particle swarm optimization. IEEE T Ind Inform 12(1):124–135. https://doi.org/10.1109/TII.2015.2500098

    Google Scholar 

  48. Rabiner LR (1986) An introduction to hidden Markov models. PLoS One 9(12):e114089. https://doi.org/10.1371/journal.pone.0114089

    Google Scholar 

  49. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286. https://doi.org/10.1109/5.18626

    Article  Google Scholar 

  50. Wang M, Wang J (2012) CHMM for tool condition monitoring and remaining useful life prediction. Int J Adv Manuf Technol 59(5–8):463–471. https://doi.org/10.1007/s00170-011-3536-7

    Article  Google Scholar 

  51. Yu J, Liang S, Tang D, Liu H (2016) A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-016-9711-0

  52. Lei Y, He Z, Zi Y (2008) A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst Appl 35(4):1593–1600. https://doi.org/10.1016/j.eswa.2007.08.072

    Article  Google Scholar 

  53. Zhang X, Wang B, Chen X (2015) Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowl-Based Syst 89:56–85. https://doi.org/10.1016/j.knosys.2015.06.017

    Article  Google Scholar 

  54. Feng Z, Liang M, Chu F (2013) Recent advances in time–frequency analysis methods for machinery fault diagnosis: a review with application examples. Mech Syst Signal Process 38(1):165–205. https://doi.org/10.1016/j.ymssp.2013.01.017

    Article  Google Scholar 

  55. SMU (2017) Experimental dataset for gear fault diagnosis of Southern Methodist University. https://goo.gl/TorZJq. Accessed 2017–07-26

  56. Zamanian AH, Ohadi A (2010) Gearbox Fault Detection through PSO Exact Wavelet Analysis and SVM Classifier. In: 18th Annual International Conference on Mechanical Engineering-ISME, Sharif University of Technology, Tehran, 11-13 May 2010. https://doi.org/10.13140/RG.2.1.4983.3442

  57. Zamanian AH, Ohadi A (2011) Gear fault diagnosis based on Gaussian correlation of vibrations signals and wavelet coefficients. Appl Soft Comput 11(8):4807–4819. https://doi.org/10.1016/j.asoc.2011.06.020

    Article  Google Scholar 

Download references

Acknowledgments

The work was supported by the “2016 Smart manufacturing project of China (2016ZXFB2002).” The authors would also like to thank the anonymous reviewers for their valuable time and efforts in review.

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmission, Chongqing University, No.174, Shazheng Street, Shapingba District, Chongqing, China

    Qiang Zhou, Ping Yan, Huayi Liu, Yang Xin & Yanzhi Chen

Authors
  1. Qiang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ping Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huayi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Xin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yanzhi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ping Yan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Q., Yan, P., Liu, H. et al. Research on a configurable method for fault diagnosis knowledge of machine tools and its application. Int J Adv Manuf Technol 95, 937–960 (2018). https://doi.org/10.1007/s00170-017-1268-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-017-1268-z

Keywords

Search

Navigation