Skip to main content
Book cover

Smart Machining Systems pp 235–266Cite as

Tool Condition Monitoring with Sparse Decomposition

  • 365 Accesses

Part of the Springer Series in Advanced Manufacturing book series (SSAM)

Abstract

In modern CNC machining, an effective tool condition monitoring (TCM) system can improve productivity, ensure workpiece quality and enhance the manufacturing intelligence (Dornfeld and Lee in Precision manufacturing. Springer, 2007; Teti et al., CIRP Annals-Manuf Tech 59:717–739, 2010). Due to its importance, tool condition monitoring (TCM) has been extensively studied (Teti et al., in CIRP Annals-Manuf Tech 59:717–739, 2010). The earlier study on TCM is mainly carried out with time series analysis, such as (Altintas in Int J Mach Tool Manuf 28:157–172, 1987) and (Kumar et al. in Int J Prod Res 35:739–751, 1997). With these methods, a threshold was set for binary state detection. However, the threshold value varies with cutting conditions and is difficult to determine.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-87878-8_7
  • Chapter length: 32 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   139.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-87878-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   179.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 7.1
Fig. 7.2
Fig. 7.3
Fig. 7.4
Fig. 7.5
Fig. 7.6
Fig. 7.7
Fig. 7.8
Fig. 7.9
Fig. 7.10
Fig. 7.11
Fig. 7.12
Fig. 7.13
Fig. 7.14
Fig. 7.15
Fig. 7.16
Fig. 7.17
Fig. 7.18
Fig. 7.19
Fig. 7.20

References

  1. Dornfeld D, Lee DE (2007) Precision manufacturing. Springer

    Google Scholar 

  2. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Annals-Manuf Tech 59(2):717–739

    Google Scholar 

  3. Altintas Y (1987) In-process detection of tool breakages using time series monitoring of cutting forces. Int J Mach Tool Manuf 28(2):157–172

    CrossRef  Google Scholar 

  4. Kumar SA, Ravindra HV, Srinivasa YG (1997) In-process tool wear monitoring through time series modeling and pattern recognition. Int J Prod Res 35(3):739–751

    CrossRef  Google Scholar 

  5. Dimla D, Lister P, Leighton N (1997) Automatic tool state identification in a metal turning operation using MLP neural networks and multivariate process parameters. Int J Mach Tool Manuf 38(4):343–352

    CrossRef  Google Scholar 

  6. Yen C-L, Lu M-C, Chen J-L (2013) Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting. Mech Syst Signal Process 34(1–2):353–366

    CrossRef  Google Scholar 

  7. Wang J, Xie J, Zhao R, Mao K, Zhang L (2016) A new probabilistic kernel factor analysis for multisensory data fusion: application to tool condition monitoring. IEEE T Instrum Meas 65(11):2527–2537

    CrossRef  Google Scholar 

  8. Soualhi A, Clerc G, Razik H et al (2016) Hidden Markov models for the prediction of impending faults. IEEE Trans Ind Electron 63(5):1781–1790

    CrossRef  Google Scholar 

  9. Geramifard O, Xu JX, Zhou JH et al (2014) Multimodal hidden markov model-based approach for tool wear monitoring. IEEE Trans Ind Electron 61(6):2900–2911

    CrossRef  Google Scholar 

  10. Ren Q, Balazinski M, Baron L et al (2014) Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling. Inf Sci 255(1):121–134

    CrossRef  Google Scholar 

  11. Pang CK, Zhou JH, Yan HC (2015) PDF and breakdown time prediction for unobservable wear using enhanced particle filters in precognitive maintenance. IEEE T Instrum Meas 64(3):649–659

    CrossRef  Google Scholar 

  12. Torabi AJ, Er MJ, Li X, Lim BS, Peen GO (2016) Application of clustering methods for online tool condition monitoring and fault diagnosis in high-speed milling processes. IEEE Syst J 10(2):721–732

    CrossRef  Google Scholar 

  13. Li W, Zhang S, Rakheja S (2016) Feature denoising and nearest-farthest distance preserving projection for machine fault diagnosis. IEEE Trans Ind Inform 12(2):393–404

    CrossRef  Google Scholar 

  14. Kannatey-Asibu E, Yum J, Kim TH (2017) Monitoring tool wear using classifier fusion. Mech Syst Signal Process 85(2):651–661

    CrossRef  Google Scholar 

  15. Bruckstein AM, Donoho DL, Elad M (2009) From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51(1):34–81

    MathSciNet  CrossRef  Google Scholar 

  16. Mallat SG (2009) A wavelet tour of signal processing: sparse way, 3rd edn. Academic Press, New York

    MATH  Google Scholar 

  17. Aharon M, Elad M, Bruckstein A (2006) The K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322

    CrossRef  Google Scholar 

  18. Mairal J, Bach F, Ponce J (2009) Online dictionary learning for sparse coding. In: Proceedings of the 26th ICML, Canada, pp 689–696

    Google Scholar 

  19. Tosic I, Frossard P (2011) Dictionary learning. IEEE Signal Process Mag 28(2):27–38

    CrossRef  Google Scholar 

  20. Cai G, Chen X, He Z (2013) Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox. Mech Syst Signal Process 41(1):34–53

    CrossRef  Google Scholar 

  21. Liu H, Liu C, Huang Y (2011) Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mech Syst Signal Process 25(2):558–574

    CrossRef  Google Scholar 

  22. Peng X, Tang Y, Du W, Qian F (2017) Multimode process monitoring and fault detection: a sparse modeling and dictionary learning method. IEEE Trans Ind Electron 64(6):4866–4875

    CrossRef  Google Scholar 

  23. Zhu K, Vogel-Heuser B (2014) Sparse representation and its applications in micro-milling condition monitoring: noise separation and tool condition monitoring. Int J Adv Manuf Technol 70(1–4):185–199

    CrossRef  Google Scholar 

  24. Yang B, Liu R, Chen X (2017) Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD. IEEE Trans Ind Inform 13(3):1321–1331

    CrossRef  Google Scholar 

  25. Gao B, Woo WL, Tian G, Zhang H (2016) Unsupervised diagnostic and monitoring of defects using waveguide imaging with adaptive sparse representation. IEEE Trans Industr Inf 12(1):405–416

    CrossRef  Google Scholar 

  26. Du Z, Chen X, Zhang H, Yan R (2015) Sparse feature identification based on union of redundant dictionary for wind turbine gearbox fault diagnosis. IEEE Trans Ind Electron 62(10):6594–6605

    CrossRef  Google Scholar 

  27. Zhu K, Lin X, Li K, Jiang L (2015) Compressive sensing and sparse decomposition in precision machining process monitoring: from theory to applications. Mechatronics 31(10):3–15

    CrossRef  Google Scholar 

  28. Mairal J, Bach F, Ponce J (2007) Discriminative learned dictionaries for local image analysis. In: IEEE CVPR, Anchorage, pp 1–8

    Google Scholar 

  29. Zhang Q, Li B (2010) Discriminative K-SVD for dictionary learning in face recognition. In: IEEE CVPR, San Francisco, pp 2691–2698

    Google Scholar 

  30. Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: IEEE ICCV, pp 543–550

    Google Scholar 

  31. Yang M, Zhang L, Feng XC, Zhang D (2014) Sparse representation based fisher discrimination dictionary learning for image classification. Int J Comput Vision 109(3):209–232

    MathSciNet  CrossRef  Google Scholar 

  32. Zhu KP, Hong GS, Wong YS, Wang WH (2008) Cutting force denoising in micro-milling tool condition monitoring. Int J Prod Res 46(16):4391–4408

    CrossRef  Google Scholar 

  33. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    MathSciNet  CrossRef  Google Scholar 

  34. Plumbley MD, Blumensath T, Daudet L, Gribonval R, Davies ME (2010) Sparse representations in audio and music: from coding to source separation. Proc IEEE 98(6):995–1005

    CrossRef  Google Scholar 

  35. Zibulevsky M, Pearlmutter BA (2001) Blind source separation by sparse decomposition in a signal dictionary. Neural Comput 13(4):863–882

    CrossRef  Google Scholar 

  36. Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2008) Supervised dictionary learning. Neural Inf Process Syst (NIPS) 21:1033–1040

    Google Scholar 

  37. Huang K, Aviyente S (2007) Sparse representation for signal classification. The Neural Inf Process Systems (NIPS) 19:609–617

    Google Scholar 

  38. Mallat SG (2008) A wavelet tour of signal processing: the sparse way, 3rd edn. Academic Press

    MATH  Google Scholar 

  39. Candès E, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509

    MathSciNet  CrossRef  Google Scholar 

  40. Cohen A, Dahmen W, DeVore R (2009) Compressed sensing and best k-term approximation. J Am Math Soc 22(1):211–231

    MathSciNet  CrossRef  Google Scholar 

  41. Theodoridis S, Koutroumbas K (2003) Pattern recognition. Academic Press

    Google Scholar 

  42. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736–3745

    Google Scholar 

  43. Lewicki MS, Sejnowski TJ (2000) Learning overcomplete representations. Neural Comput 12(2):337–365

    CrossRef  Google Scholar 

  44. Efron B, Johnstone I, Hastie T, Tibshirani R (2002) Least angle regression. Ann Stat 32(2):407–499

    MathSciNet  MATH  Google Scholar 

  45. Duda RO, Hart PE, Stork DS (2001) Pattern classification, 2nd edn. Wiley

    Google Scholar 

  46. Pati YC, Rezaiifar R, Krishnaprasad PS (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: The 27th annual Asilomar conference on signals, systems, and computers, pp 40–44

    Google Scholar 

  47. Zhu KP, Mei T, Ye DS (2015) Online condition monitoring in micro-milling: a force waveform shape analysis approach. IEEE Trans Ind Electron 62(6):3806–3813

    Google Scholar 

  48. Jemielniak K, Arrazola PJ (2007) Application of AE and cutting force signals in tool condition monitoring in micro-milling. CIRP J Manuf Sci Tec 1(2):97–102

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Precision Manufacturing Laboratory, Institute of Advanced Manufacturing Technology, Chinese Academy of Sciences, Changzhou, Jiangsu, China

    Kunpeng Zhu

  2. Institute of Precision Manufacturing, Wuhan University of Science and Technology, Wuhan, Hubei, China

    Kunpeng Zhu

Authors
  1. Kunpeng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kunpeng Zhu .

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Zhu, K. (2022). Tool Condition Monitoring with Sparse Decomposition. In: Smart Machining Systems. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-030-87878-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87878-8_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87877-1

  • Online ISBN: 978-3-030-87878-8

  • eBook Packages: EngineeringEngineering (R0)