A comparative study of feature selection for hidden Markov model-based micro-milling tool wear monitoring

Machining Science and Technology

Volumn 12, Issue 3, 2008, Pages 348-369

  Zhu, K P ^a   Hong, G S ^a   Wong, Y S ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Feature selection; Micro milling; Tool wear monitoring

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONDITION MONITORING; COPPER; DISCRIMINANT ANALYSIS; EVOLUTIONARY ALGORITHMS; FINANCIAL DATA PROCESSING; HIDDEN MARKOV MODELS; ISOMERS; LEARNING SYSTEMS; MARKOV PROCESSES; MILLING (MACHINING); MILLING MACHINES; OBJECT RECOGNITION; SENSOR NETWORKS; SEPARATION;

AUTOMATIC RELEVANCE DETERMINATION; CLASSIFICATION RATES; COMPARATIVE STUDIES; FEATURE SELECTION; FEATURE SELECTION METHODS; FEATURE SPACES; FISHER'S LINEAR DISCRIMINANT ANALYSIS; HIDDEN MARKOV MODELING; HIDDEN-MARKOV MODEL; MICRO-MILLING; PHYSICAL MEANINGS; PRINCIPAL COMPONENT ANALYSIS (PCA); REDUNDANT INFORMATIONS; ROBUST MODELING; SEPARATION ABILITY; STATE DISCRIMINATION; STATE SEPARATION; TOOL CONDITION MONITORING; TOOL WEAR MONITORING; WORKING CONDITIONS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 51849160407     PISSN: 10910344     EISSN: 15322483     Source Type: Journal    
DOI: 10.1080/10910340802293769     Document Type: Article

References (42)

1. Alaniz-Lumbreras, P.D.; Gómez-Loenzo, R.A.; Romero-Troncoso, R.J.; Peniche-Vera, del Rocío, R.; Jáuregui-Correa, J.C.; Herrera-Ruiz, G. (2006) Sensorless detection of tool breakage in milling. Machining Science and Technology, 10: 263-274.
2. Baruah, P.; Chinnam, R.B. (2005) HMMs for diagnostics and prognostics in machining processes. International Journal of Production Research, 43 (15): 1275-1293.
3. Chu, W.; Keerthi, S.S.; Ong, C.J.; Ghahramani, Z. (2006) Bayesian support vector machines for feature ranking and selection. In I. Guyon; S. Gunn; M. Nikravesh; L. Zadeh, editors, Feature Extraction, Foundations and Applications, Series Studies in Fuzziness and Soft Computing. Physica-Verlag, Springer.
4. Duda, R.O.; Hart P.E.; Stork, D.G. (2001) Pattern Classification, (2nd ed.), Wiley Interscience, New York.
5. Errtunc, H.M.; Looparo K.A.; Ocak, H. (2001) Tool wear condition monitoring in drilling operations using hidden Markov models. International Journal of Machine Tools and Manufacture, 41: 1363-1384.
6. Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition, 2nd ed., Academic Press, San Diego, CA.
7. Goebel K.; Yan, W. (2001) Feature selection for tool wear diagnosis using soft computing techniques. GE Technical Information Series, 2001CRD011.
8. Houshmand, A.A.; Kannatey-Asibu, E. Jr. (1989) Statistical process control of acoustic emission for cutting tool monitoring. Mechanical Systems and Signal Processing, 3 (4): 405-424.
9. Iannarilli, F.J.; Rubin, P.A. (2003) Feature selection for multiclass discrimination via mixed-integer linear programming. IEEE Transitions Pattern Analysis and Machine Intelligence, 25 (6): 779-783.
10. Jolliffe, I.T. (2002) Principal Component Analysis. 2nd edition, Springer-Verlag, New York.
11. Li, D.; Douglas, O.S. (2003) Speech Processing: A Dynamic and Object-Oriented Approach. Marcel Dekker, Inc., New York.
12. Li, S.; Elbestawi, M.A. (1996) Fuzzy clustering for automated tool condition monitoring in machinining. Mechanical Systems and Signal Processing, 10 (5): 533-550.
13. Liao, T.W.; Hua, G.; Qu, J.; Blau, P.J. (2006) Grinding wheel condition monitoring with hidden Markov model-based clustering methods, Machining Science and Technology 10: 511-538.
14. Liu, X.; Devor, R.E.; Kapoor, S.G.; Ehmann, K.F. (2004) The mechanics of machining at the micro-scale: assessment of the current state of the science. Journal of Manufacturing Science and Engineering, 126 (4): 666-677.
15. Liu, B.; Ling, S.F. (1999) On the selection of informative wavelets for machinery diagnosis. Mechanical Systems and Signal Processing, 13 (1): 145-162.
16. MacKay, D.J. (1992) A practical Bayesian framework for back-propagation networks. Neural Computation, 4 (3): 448-472.
17. Malhi, A.; Gao, R.X. (2004) PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, 53 (6): 1517-1525.
18. Mallat, S.G. (1998) A Wavelet Tour of Signal Processing. Academic Press, San Diego, CA.
19. Mao, K.Z. (2005) Identifying critical variables of principal components for unsupervised feature selection. IEEE Transactions on Systems, Man, And Cybernetics-Part B: Cybernetics, 35 (2): 339-344.
20. Neal, R.M. (1996) Bayesian Learning for Neural Networks. No. 118 Lecture Notes in Statistics. Springer, New York.
21. Niu, Y.M.; Wong, Y.S.; Hong, G.S. (1998) Multi-category classification of tool conditions using wavelet packets and ARTZ network, ASME. Journal of Manufacturing Science and Technology, 120 (4): 807-815.
22. Penny, W.D.; Roberts, S.J. (1999) Bayesian neural networks for classification: how useful is the evidence framework? Neural Networks, 12: 877-892.
23. Pittner, S.; Kamarthi, S.V. (1999) Feature extraction from wavelet coefficients for pattern recognition tasks. IEEE Transactions on Pattern and Machine Intelligence, 21 (1): 1484-1489.
24. Rabiner, L.R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE, 77 (2): 257-286.
25. Rahman, R.; Kumar, AS.; Prakash, J.R.R (2001) Micro-milling of pure copper, International Journal of Materials Processing, 16: 39-43.
26. Randall K.F.; Ostendorf, M.; Bernard, G.D.; Castanon, D.A. (2003) Multilevel classification of milling tool wear with confidence estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (1): 75-86.
27. Saito, N.; Coifman, R.R.; Geshwind, F.B.; Warner, F. (2002) Discriminant feature extraction using empirical probability density estimation and a local basis library. Pattern Recognition, 35: 2841-2852.
28. Shaw, M.C. (2005) Metal Cutting Principles, 2nd ed., Oxford University Press, Oxford, UK.
29. Sun, J.; Hong, G.S.; Rahman, M.; Wong, Y.S. (2004) Identification of feature set for effective tool condition monitoring by acoustic emission sensing. International Journal Production Research, 42 (5): 901-918.
30. Tansel, I.N.; Arkan, T.T.; Bao, W.Y.; Mahendrakar, N.; Shisler, B.; Smith, D.; McCool, M. (2000) Tool wear estimation in micro-machining. Part I: tool usage-cutting force relationship. International Journal of Machine Tools and Manufacture, 40: 599-608.
31. Tansel, I.N.; Rodriguez, O.; Trujillo, M.; Paz, E.; Li, W (1998) Micro-end-milling-I. wear and breakage. International Journal of Machine Tools and Manufacture, 38: 1419-1436.
32. Theodoridis, S.; Koutroumbas, K (2003) Pattern Recognition. Academic Press, San Diego, CA.
33. Turner, I.Y.; Wood, K.L.; Viahniac, J.B. (2000) Monitoring of signals from the Karhunen-Loeve transform. Mechanical Systems and Signal Processing, 14 (6): 1011-1026.
34. Wang, W.H.; Hong, G.S.; Wong, Y.S.; Zhu, K.P (2007) Sensor fusion for on-line tool condition monitoring in milling. International Journal of Production Research, 45 (21): 5095-5116.
35. Wang, W.; Jones, P.; Partridge, D. (2001) A comparative study of feature-salience ranking techniques. Neural Computation, 13: 1603-1623.
36. Wang, L.; Mehrabi, M.G.; Elijah, K.A. (2002) Hidden Markov model-based tool wear monitoring in turning. ASME Journal of Manufacturing Science and Engineering, 124: 652-658.
37. Wickerhauser, M.V.; Coifman, R.R. (1992) Entropy based methods for best basis selection. IEEE Transitions Information Technology, 38 (2): 719-746.
38. Wu, Y.; Du, R. (1996) Feature extraction and assessment using wavelet packets for monitoring of machining process. Mechanical Systems and Signal Processing, 10 (1): 29-53.
39. Yen, G.G.; Lin, K.C. (2000) Wavelet packet feature extraction for vibration monitoring. IEEE Transitions on Industrial Electronics, 47 (3): 650-667.
40. Zhu, K.P.; Hong, G.S.; Wong, Y.S.; Wang, W.H. (2008a) Cutting force denoising in micro-milling tool condition monitoring. International Journal of Production Research, 46 (16): 4391-4408.
41. Zhu, K.P.; Wong, Y.S.; Hong, G.S. (2008b) Multi-category micro-milling tool wear classification with continuous hidden Markov models. Mechanical System and Signal Processing, pp. 1-14, In Press, doi:10.1016/j.ymssp.2008. 04.010.
42. Zhu, K.P; Wong, Y.S.; Hong, G.S. (2008c) Noise-robust tool condition monitoring in micro-milling with hidden Markov models. Soft Computing Applications in Industry, pp. 23-46, Springer.