Multirate coupled hidden Markov models and their application to machining tool-wear classification

IEEE Transactions on Signal Processing

Volumn 55, Issue 6 II, 2007, Pages 2885-2896

  Çetin, Öz̈ur ^a   Ostendorf, Mari ^b   Bernard, Gary D ^c  

^a INTERNATIONAL COMPUTER SCIENCE INSTITUTE   (United States)

^b UNIVERSITY OF WASHINGTON   (United States)

^c BOEING COMPANY   (United States)

Author keywords

Confidence; Hidden Markov model (HMM); Milling; Multirate HMM; Multiscale statistical modeling; Tool wear; Tool wear monitoring

Indexed keywords

ACOUSTIC EMISSIONS; HIDDEN MARKOV MODELS; INFERENCE ENGINES; MACHINE TOOLS; MATHEMATICAL MODELS; PARAMETER ESTIMATION;

MULTIRATE COUPLED HIDDEN MARKOV MODELS; MULTISCALE STATISTICAL MODELING; TOOL-WEAR MONITORING;

ACOUSTIC SIGNAL PROCESSING;

EID: 34249812683     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2007.893972     Document Type: Article

References (54)

1. L. Atlas, M. Ostendorf, and G. D. Bernard, "Hidden Markov models for monitoring machine tool wear," in Proc. Int. Conf. Acoustics, Speech and Signal Processing, 2001, pp. 3887-3890.
2. M. Basseville, A. Benveniste, K. C. Chou, S. A. Golden, R. Nikoukhah, and A. S. Willsky, "Modeling and estimation of multiresolution stochastic processes," IEEE Trans. Inf. Theory, vol. 38, no. 2, pp. 766-784, Mar. 1992.
3. Y. Bengio and P. Frasconi, "Diffusion of context and credit information in Markovian models," J. Artif. Intell. Res., vol. 3, pp. 249-270, 1995.
4. H. Bourlard and S. Dupont, "A new ASR approach based on independent processing and recombination of partial frequency bands," in Proc. Int. Conf. Spoken Language Processing, 1996, pp. 426-429.
5. M. Brand and N. Oliver, "Coupled hidden Markov models for complex action recognition," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1997, pp. 994-999.
6. Ö. Çetin, "Multi-rate modeling, model inference, and estimation for statistical classifiers," Ph.D. dissertation, Univ. Washington, Seattle, 2004.
7. Ö. Çetin, and M. Ostendorf, "Multi-rate hidden Markov models for monitoring machining tool wear," Tech. Rep. UWEETR-2004-0011, Dept. Elect. Eng., Univ. Washington, Seattle, 2003.
8. Ö. Çetin, and M. Ostendorf, "Multi-rate hidden Markov models and their application to machining tool-wear classification," in Proc. Int. Conf. Acoustics, Speech, Signal Processing, 2004, pp. 837-840.
9. K. C. Chou and L. P. Heck, "A multiscale stochastic modeling approach to the monitoring of mechanical systems," in Proc. IEEE Int. Symp. Time-Frequency and Time-Scale Analysis, 1994, pp. 25-27.
10. K. C. Chou, A. S. Willsky, and A. Benveniste, "Multiscale recursive estimation, data fusion, and regularization," IEEE Trans. Autom. Control, vol. 39, no. 3, pp. 464-478, Mar. 1994.
11. R. G. Cowell, A. P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter, Probabilistic Networks and Expert Systems. New York: Springer-Verlag, 1999.
12. M. S. Crouse, R. D. Nowak, and R. G. Baraniuk, "Wavelet-based statistical signal processing using hidden Markov models," IEEE Trans. Signal Process., vol. 46, no. 4, pp. 886-902, Apr. 1998.
13. L. Dan and J. Mathew, "Tool wear and failure monitoring techniques for turning - A review," Int. J. Mach. Tools Manufact., vol. 30, no. 4, pp. 579-598, 1990.
14. Snr. D. E. Dimla, "Sensor signal for tool-wear monitoring in metal cutting operations - A review of methods," Int. J. Mach. Tools Manufact., vol. 40, pp. 1073-1098, 2000.
15. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Roy. Statist. Soc. B, vol. 39, pp. 185-197, 1977.
16. S. Dupont and H. Bourlard, "Using multiple time scales in a multistream speech recognition system," in Proc. Eurospeech, 1997, pp. 3-6.
17. D. Edwards and S. L. Lauritzen, "The TM algorithm for maximizing a conditional likelihood function," Biometrika, vol. 88, pp. 961-972, 2001.
18. Y. Ephraim and N. Merhav, "Hidden Markov processes," IEEE Trans. Inf. Theory, vol. 48, no. 6, pp. 1518-1569, Jun. 2002.
19. H. M. Ertunc, K. A. Loparo, and H. Ocak, "Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs ," Int. J. Mach. Tools Manufact., vol. 41, pp. 1363-1384, 2001.
20. E. O. Ezugwu and Z. M. Wang, "Titanium alloys and their machinability - A review," Int. J. Mater. Process. Technol., vol. 68, pp. 262-274, 1997.
21. L. Fouque, A. Appriou, and W. Pieczynski, "Multiresolution hidden Markov chain model and unsupervised image segmentation," in Proc. 4th IEEE Southwest Symp. Image Analysis and Interpretation, 2000, pp. 121-125.
22. S. Fine, Y. Singer, and N. Tishby, "The hierarchical hidden Markov model: Analysis and applications," Mach. Learn., vol. 32, no. 1, pp. 41-62, 1998.
23. R. K. Fish, "Dynamic models of machining vibrations, designed for classification of tool wear," Ph.D. dissertation, Univ. Washington, Seattle, 2001.
24. R. K. Fish, M. Ostendorf, G. D. Bernard, and D. A. Castanon, "Multilevel classification of milling tool wear with confidence estimation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 1, pp. 75-85, Jan. 2003.
25. Z. Ghahramani and M. I. Jordan, "Factorial hidden Markov models," Mach. Learn., vol. 29, pp. 245-273, 1997.
26. L. Gillick and S. J. Cox, "Some statistical issues in the comparison of speech recognition algorithms," in Proc. Int. Conf. Acoustics, Speech, Signal Processing, 1989, pp. 532-535.
27. L. P. Heck and J. H. McClellan, "Mechanical system monitoring using hidden Markov models," in Proc. Int. Conf. Acoustics, Speech, Signal Processing, 1991, pp. 1697-1700.
28. R. V. Hogg, A. T. Craig, and J. McKean, Introduction to Mathematical Statistics. Englewood Cliffs, NJ: Prentice-Hall, 2004.
29. T. Jaakkola and D. Haussler, "Exploiting generative models in discriminative classifiers," in Advances in Neural Information Processing Systems II. Cambridge, MA: MIT Press, 1999, pp. 487-493.
30. E. Jantunen, "A summary of methods applied to tool condition monitoring in drilling," Int. J. Mach. Tools Manufact., vol. 42, pp. 997-1010, 2002.
31. B. H. Juang and S. Katagiri, "Discriminative learning for minimum error classification," IEEE Trans. Signal Process., vol. 40, no. 12, pp. 3043-3053, Dec. 1992.
32. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data," in Proc. Int. Conf. Machine Learning, 2001, pp. 591-598.
33. S. L. Lauritzen, Graphical Models. Oxford, U.K.: Oxford Univ. Press, 1996.
34. J. Li, R. M. Gray, and R. A. Olshen, "Multiresolution image classification by hierarchical modeling with two dimensional hidden Markov models," IEEE Trans. Inf. Theory, vol. 46, no. 5, pp. 1826-1841, Aug. 2000.
35. M. R. Luettgen, W. C. Karl, and A. S. Willsky, "Multiscale representations of Markov random fields," IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3377-3396, Dec. 1993.
36. J. Luettin, G. Potamianos, and C. Neti, "Asynchronous stream modeling for large vocabulary audio-visual speech recognition," in Proc. Int. Conf. Acoustics, Speech, Signal Processing, 2001, pp. 169-172.
37. P. McCullagh and J. A. Nelder, Generalized Linear Models. New York: Chapman & Hall, 1989.
38. K. P. Murphy, "Dynamic Bayesian networks: Representation, inference and learning," Ph.D. dissertation, Univ. California, Berkeley, 2002.
39. H. J. Nock and M. Ostendorf, "Parameter reduction schemes for loosely coupled HMMs," Comput. Speech Lang., vol. 17, pp. 233-262, 2003.
40. M. Ostendorf, L. Atlas, R. Fish, Ö. Çetin, S. Sukittanon, and G. D. Bernard, "Joint use of dynamical classifiers and ambiguity plane features," in Proc. Int. Conf. Acoustics, Speech, Signal Processing 2001, pp. 3589-3592.
41. L. Owsley, L. Atlas, and G. Bernard, "Self-organizing feature maps and hidden Markov models for machine-tool monitoring," IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2787-2798, Nov. 1997.
42. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann, 1991.
43. W. Pieczynski, "Pairwise Markov chains," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 634-639, May 2003.
44. W. Pieczynski and F. Desbouvries, "On triplet Markov chains," presented at the Int. Symp. Applied Stochastic Models and Data Analysis, 2005.
45. P. W. Prickett and C. Johns, "An overview of approaches to end milling tool monitoring," Int. J. Mach. Tools Manufacat., vol. 39, pp. 105-122, 1999.
46. L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
47. R. Shachter, "Bayes-Ball: The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams)," in Proc. Uncertainty in Artificial Intelligence, 1998, pp. 480-487.
48. L. K. Saul and M. I. Jordan, "Mixed memory Markov models: Decomposing complex stochastic processes as mixtures of simple ones," Mach. Learn., vol. 37, no. 1, pp. 75-87, 1998.
49. H. Shivakumar, "Prediction of tool wear likelihood for milling applications," M.S. thesis, Boston Univ., Boston, MA, 2000.
50. B. Sick, "On-line and indirect tool wear monitoring in turning with artificial neural networks: A review of more than a decade of research," Mech. Syst. Signal Process., vol. 16, no. 4, pp. 487-546, 2002.
51. M. Siu and H. Gish, "Evaluation of word confidence for speech recognition systems," Comput. Speech Lang., vol. 13, pp. 299-319, 1999.
52. L. Wang, M. G. Mehrabi, and E. Kannatey Asibu, Jr., "Hidden Markov model-based tool wear monitoring in turning," J. Manufact. Sci. Eng. vol. 124, no. 3, pp. 651-658, 2002.
53. A. S. Willsky, "Multiresolution Markov models for signal and image processing," Proc. IEEE, vol. 90, no. 8, pp. 1396-1458, Aug. 2002.
54. S. Young, G. Evermann, D. Kershaw, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, and P. Woodland, The HTK Book. Cambridge, U.K.: Cambridge Univ. Press, 2002, (for HTK Version 3.2).