This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Advances in Real-Time Monitoring of Acoustic Emissions
Annotation ability available
Sector:
Language:
English
Abstract
We are developing a flexible and general methodology for real-time monitoring of acoustic emissions in machining applications. The goal of this work is to develop an approach to in-process monitoring which allows continuous assessment of tool wear and early warning of process exceptions. The nature of metal removal processes creates short-lived vibrations that carry information about the condition of the cutting tool and quality of cut. We wish to extract and represent these transient events without loss of important spectral structure. Other challenges include the need for system training data selection in the absence of expert labeled data, the modeling of short-term time evolution, and efficient real-time operation on an inexpensive computing platform. We present a system that meets these challenges through the use of high resolution time-frequency representations, vector quantization, hidden Markov models and a novel method of regrouping training data to refine initial class guesses. Applying this system to the classification of milling transients, we show that our system is capable of extracting these events and assigning them to meaningful classes - a crucial step in monitoring tool wear.
Authors
Citation
McLaughlin, J., Owsley, L., Atlas, L., and Bernard, G., "Advances in Real-Time Monitoring of Acoustic Emissions," SAE Technical Paper 972254, 1997, https://doi.org/10.4271/972254.Also In
References
- Huang X. Acero A. Alleva F. Hwang M.Y. Li J. “Microsoft Windows Highly Intelligent Speech Recognizer: Whisper,” Int. Conf. on Acoust., Speech and Sig. Proc. 93 6 1 May 1995
- Reynolds D. Rose R. “Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models,” IEEE Trans. on Speech and Audio Processing 3 1 72 82 January 1995
- Narayanan Siva Bala Fang Jing Bernard Gary D. Atlas Les E. “Feature Representations for Monitoring of Tool Wear,” Proc. of ICASSP'94 Adelaide, Australia 6 137 140 April 1994
- Fang J. Atlas L. Bernard G. “Advantages of Quadratic Detectors for Analysis of Manufacturing Sensor Data,” IEEE Symposium on Time-Frequency and Time Scale Analysis 345 348 Victoria, BC 1992
- Zheng K. Whitehouse D.J. “The Application of the Wigner Distribution to Machine Tool Monitoring,” Proc. Inst. Mech. Engrs 206 249 264 1992
- Heck L. P. “Signal Processing Research in Automatic Tool Wear Monitoring,” Proceedings of ICASSP 93 1 55 58 1993
- Braun S. Lenz E. Wu C.L. “Signature Analysis Applied to Drilling,” ASME J. of Engr. for Industry 104 268 276 1982
- Frarey J.L. “Have We Finished?,” Sound and Vibration 29 10 5 1995
- Dornfeld D.A. Koeing W. Ketteler G. “Present State of Tool and Process Monitoring in Cutting,” Proceedings of the International CIRP/VDI Conference September 1993
- Duda, R.O. Hart, P.E. Pattern Classification and Scene Analysis New York John Wiley & Sons 1973
- Loughlin P. “Time-Frequency Energy Density Functions: Theory and Synthesis” University of Washington Seattle, WA 1992
- Loughlin P. Pitton J. Atlas L. “An information-theoretic approach to positive time-frequency distributions,” Proc. ICASSP 92 V 125 128 1992
- Vincent I. Doncarli C. Le Carpentier E. “Non Stationary Signals Classification Using Time-Frequency Distributions,” Proc. of the Int. Sym. of Time-Frequency and Time-Scale Analysis 233 236 June 1996
- Kohenen T. “The Self-Organizing Map,” Proceeding of the IEEE 78 1464 1990
- Kohenen T. Self-Organization and Associative Memory Springer-Verlag Berlin 1989
- Atlas L. Owsley L. McLaughlin J. Bernard G. “Automatic Feature-Finding for Time-Frequency Distributions,” Proc. IEEE Symposium on Time-Frequency and Time Scale Analysis 333 336 Paris, France 1996
- Owsley Atlas L. Bernard G. “Feature Extraction Networks for Dull Tool Monitoring,” IEEE Proc. ICASSP'95 3355 3358 Detroit, MI 1995
- Rabiner L.R. Juang B.H. “An Introduction to Hidden Markov Models,” IEEE ASSP Magazine 4 16 January 1986
- Huang X.D. Ariki Y. Jack M. A. Hidden Markov Models for Speech Recognition Edinburgh Information Technology Series Edinburgh University Press Edinburgh 1990