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Erschienen in:
Introduction to the Smart Machining System Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

7. Tool Condition Monitoring with Sparse Decomposition

verfasst von : Kunpeng Zhu

Erschienen in: Smart Machining Systems

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Signal Processing for Machining Process Modeling and Condition Monitoring
Nächstes Kapitel Machine Vision Based Smart Machining System Monitoring
Literatur
1.
Dornfeld D, Lee DE (2007) Precision manufacturing. Springer
2.
Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Annals-Manuf Tech 59(2):717–739
3.
Altintas Y (1987) In-process detection of tool breakages using time series monitoring of cutting forces. Int J Mach Tool Manuf 28(2):157–172CrossRef
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):739–751CrossRef
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):343–352CrossRef
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):353–366CrossRef
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):2527–2537CrossRef
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):1781–1790CrossRef
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):2900–2911CrossRef
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):121–134CrossRef
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):649–659CrossRef
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):721–732CrossRef
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):393–404CrossRef
14.
Kannatey-Asibu E, Yum J, Kim TH (2017) Monitoring tool wear using classifier fusion. Mech Syst Signal Process 85(2):651–661CrossRef
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):34–81MathSciNetCrossRef
16.
Mallat SG (2009) A wavelet tour of signal processing: sparse way, 3rd edn. Academic Press, New YorkMATH
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):4311–4322CrossRef
18.
Mairal J, Bach F, Ponce J (2009) Online dictionary learning for sparse coding. In: Proceedings of the 26th ICML, Canada, pp 689–696
19.
Tosic I, Frossard P (2011) Dictionary learning. IEEE Signal Process Mag 28(2):27–38CrossRef
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):34–53CrossRef
21.
Liu H, Liu C, Huang Y (2011) Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mech Syst Signal Process 25(2):558–574CrossRef
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):4866–4875CrossRef
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):185–199CrossRef
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):1321–1331CrossRef
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):405–416CrossRef
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):6594–6605CrossRef
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):3–15CrossRef
28.
Mairal J, Bach F, Ponce J (2007) Discriminative learned dictionaries for local image analysis. In: IEEE CVPR, Anchorage, pp 1–8
29.
Zhang Q, Li B (2010) Discriminative K-SVD for dictionary learning in face recognition. In: IEEE CVPR, San Francisco, pp 2691–2698
30.
Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: IEEE ICCV, pp 543–550
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):209–232MathSciNetCrossRef
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):4391–4408CrossRef
33.
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):995–1005CrossRef
35.
Zibulevsky M, Pearlmutter BA (2001) Blind source separation by sparse decomposition in a signal dictionary. Neural Comput 13(4):863–882CrossRef
36.
Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2008) Supervised dictionary learning. Neural Inf Process Syst (NIPS) 21:1033–1040
37.
Huang K, Aviyente S (2007) Sparse representation for signal classification. The Neural Inf Process Systems (NIPS) 19:609–617
38.
Mallat SG (2008) A wavelet tour of signal processing: the sparse way, 3rd edn. Academic PressMATH
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):489–509MathSciNetCrossRef
40.
Cohen A, Dahmen W, DeVore R (2009) Compressed sensing and best k-term approximation. J Am Math Soc 22(1):211–231MathSciNetCrossRef
41.
Theodoridis S, Koutroumbas K (2003) Pattern recognition. Academic Press
42.
Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736–3745
43.
Lewicki MS, Sejnowski TJ (2000) Learning overcomplete representations. Neural Comput 12(2):337–365CrossRef
44.
Efron B, Johnstone I, Hastie T, Tibshirani R (2002) Least angle regression. Ann Stat 32(2):407–499MathSciNetMATH
45.
Duda RO, Hart PE, Stork DS (2001) Pattern classification, 2nd edn. Wiley
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
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
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):97–102CrossRef
Metadaten
Titel
Tool Condition Monitoring with Sparse Decomposition
verfasst von
Kunpeng Zhu
Copyright-Jahr
2022
Buch
Smart Machining Systems

Print ISBN: 978-3-030-87877-1

Electronic ISBN: 978-3-030-87878-8

Copyright-Jahr: 2022

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

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