A Hybrid Approach of ANN and HMM for Cutting Chatter Monitoring

Chun Liang Zhang; Xia Yue; Yong Tao Jiang; Wei Zheng

doi:10.4028/www.scientific.net/AMR.97-101.3225

Paper Titles

Modeling of Mircrostructure Evolution in Directional Solidification of Ni-Based Superalloy Turbine Blade
p.3207

Thermal Error Robust Modeling and Real Time Compensation Based on the GA-BPN Theory on a Vertical Machine Centre
p.3211

Stability Margin Sensitivity Analysis of a Multi-Rotor System Geared by Spiral Bevel Gear Pairs
p.3215

Structural Optimization of Non-Contact Vortex Negative Pressure Carrier
p.3219

A Hybrid Approach of ANN and HMM for Cutting Chatter Monitoring
p.3225

State Monitoring for Centrifugal Pump of PWR Based on HMM and SVM
p.3233

The Influence of Interfacial Thermal Contact Conductance on Resistance Spot Weld Nugget Formation
p.3239

A Particle Swarm Algorithm for Assembly Sequence Planning
p.3243

A Study of the Microstructure and Grain Size at the Welding Heat Affected Zone of an Industrial Pure Aluminum
p.3247

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 97-101A Hybrid Approach of ANN and HMM for Cutting...

A Hybrid Approach of ANN and HMM for Cutting Chatter Monitoring

Abstract:

The Hidden Markov Model (HMM) offers a powerful framework for temporal modeling of features extracted from time varying signals, and the Artificial Neural Network (ANN) has been widely used for pattern recognition, time series prediction, and optimization and forecasting. This paper describes a hybrid HMM/ANN approach which is a very competitive alternative to standard HMM for cutting chatter monitoring both in terms of performances and recognition accuracy. The hybrid HMM/ANN system uses ANN, usually a Multi-layer Perceptron ANN, to integrate the multi-stream inputs as feature transformation, whose goal is to take the advantages from the properties of both HMM and ANN. Experimental results show the efficiency of the hybrid system in monitoring of cutting process.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 97-101)

Pages:

3225-3232

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.97-101.3225

Citation:

Cite this paper

Online since:

March 2010

Authors:

Chun Liang Zhang, Xia Yue, Yong Tao Jiang, Wei Zheng

Keywords:

Artificial Neural Network (ANN), Condition Monitoring, Cutting Chatter, HMM

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] Heck LP, McClellan JH: Mechanical system monitoring using hidden Markov models. IEEE Int Conf Acoustics, Speech, and Signal Processing (1991), P. 1697-1700.

DOI: 10.1109/icassp.1991.150631

Google Scholar

[2] H. Bourlard, N. Morgan: Connectionist Speech Recognition - A Hybrid Approach. Kluwer Academic Press (1994. ).

Google Scholar

[3] H. Ney: Speech Recognition in a Neural Network Framework: Discriminative Training of Gaussian Models and Mixture Densities as Radial Basis Functions. IEEE Int Conf Acoustics, Speech, and Signal Processing (1991), P. 573-576.

DOI: 10.1109/icassp.1991.150404

Google Scholar

[4] Roberto Gemello: Hybrid HMM Neural Network based Speech Recognition in Loquendo ASR, http: /www. loquendo. com/en/brochure/Speech_Recognition_ASR. pdf.

Google Scholar

[5] Gerhard Rigoll: Maximum Mutual Information Neural Networks for Hybrid Connectionist-HMM. Speech Recognition Systems. IEEE Transactions on speech and audio processing, Vol. 2(1994), pp.175-184.

DOI: 10.1109/89.260360

Google Scholar

[6] Gerhard Rigoll, Daniel Willett: A NN/HMM hybrid for continuous speech recognition with a discriminant nonlinear feature extraction. IEEE Int Conf Acoustics, Speech, and Signal Processing (1998), P. 9-12.

DOI: 10.1109/icassp.1998.674354

Google Scholar

[7] Daniel Willett, Gerhard Rigoll: Hybrid NN/HMM-Based Speech Recognition with a Discriminant Neural Feature Extraction. Proceedings of the 1997 conference on Advances in Neural Information Processing Systems 10(1998), pp.763-769.

DOI: 10.1109/icassp.1998.674354

Google Scholar

[8] L. Bahl, P. Brown, P. de Souza and R. Mercer: Maximum mutual information estimation of hidden Markov model parameters for speech recognition. IEEE Int Conf Acoustics, Speech, and Signal Processing (1986), P. 49-52.

DOI: 10.1109/icassp.1986.1169179

Google Scholar

[9] J.M. Lee, S.J. Kim, Y. Hwang: Mechanical Signal Analysis Using Hidden Markov Model. International Congress on Sound and Vibration 9(2002).

Google Scholar

[10] J.M. Lee, S.J. Kim, Y. Hwang: Diagnosis of mechanical fault signal using continuous hidden Markov model. Journal of Sound and Vibration. Vol. 276(2004), pp.1065-1080.

DOI: 10.1016/j.jsv.2003.08.021

Google Scholar

[11] N. Baydar, A. Ball: Detection of Gear Failures via Vibration and Acoustic Signals Using Wavelet Transform. Mechanical Systems and Signal Processing. Vol. 17(2003), pp.787-804.

DOI: 10.1006/mssp.2001.1435

Google Scholar

[12] Y. Xu, M. Ge: Hidden Markov model-based process monitoring system. Journal of Intelligent Manufacturing. Vol. 15(2004), pp.337-350.

DOI: 10.1023/b:jims.0000026572.03164.64

Google Scholar

[13] Cho Wongyu, Lee Kim. Modeling and recognition of cursive words with Hidden Markov Models. Pattern Recognition. Vol. 28(1991), p.1945-(1953).

DOI: 10.1016/0031-3203(95)00041-0

Google Scholar

[14] V. Purushotham, S. Narayanan, Suryanarayana A.N. Prasad: Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. NDT&E International 38(2005), pp.654-664.

DOI: 10.1016/j.ndteint.2005.04.003

Google Scholar