Abstract
Condition classification is an important step in machinery fault detection, which is a problem of pattern recognition. Currently, there are a lot of techniques in this area and the purpose of this paper is to investigate two popular recognition techniques, namely hidden Markov model and support vector machine. At the beginning, we briefly introduced the procedure of feature extraction and the theoretical background of this paper. The comparison experiment was conducted for gearbox fault detection and the analysis results from this work showed that support vector machine has better classification performance in this area.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Burges, C. J. C., 1998, “A tutorial on Support Vector Machines for Pattern Recognition,”Data Mining and Knowledge Discovery, Vol. 2, pp. 121–167.
Byington, C. S., Kozlowski, J. D., 2000, “Transitional Data for Estimation of Gearbox Remaining Useful Life,”Mechanical Diagnostic Test Bed Data,Applied Research Laboratory, The Pennsylvania State University.
Ertunc, H. M., Loparo, K. A., Ocak, H., 2001, “Tool Wear Condition Monitoring in Drilling Operations Using Hidden Markov Models(HMM),”International Journal of Machine Tools & Manufacture, Vol. 41, pp. 1363–1384.
Huo, Q., Chan, C., Lee, C. H., 1995, “Baysian Adaptive Learning of the Parameters of Hidden Markov Model for Speech Recognition,”IEEE Transaction on Speech and Audio Processing, Vol. 3, No. 5, pp. 334–345.
Justino, E. J. R., Bortolozzi, F., Sabourin, R., 2005, “A Comparison of SVM and HMM Classifiers in the Off-Line Signature Verification,”Pattern Recognition Letters, Vol. 26, pp. 1377–1385.
Loutriis, S., Trochidis, A., 2004, “Classification of Gear Faults Using Hoelder Exponents,”Mechanical Systems and Signal Processing, Vol. 18, pp. 1009–1030.
Mallat, S., Hwang, W. L., 1992, “Singularity Detection and Processing with Wavelets,”IEEE Transactions on Information Theory, Vol. 38, No. 2, pp. 617–643.
Miao, Q., Makis, V., 2007, “Condition Monitoring and Classification of Rotating Machinery Using Wavelets and Hidden Markov Models,”Mechanical Systems and Signal Processing, Vol. 21, No. 2, pp. 840–855.
Miao, Q., 2005, “Application of Wavelets and Hidden Markov Model in Condition-Based Maintenance,”Ph.D Thesis, University of Toronto.
Ocak, H., Loparo, K. A., 2001, “A New Bearing Fault Detection and Diagnosis Scheme Based on Hidden Markov Modeling of Vibration Signals,”IEEE ICASSP 2001, Vol. 5, pp. 3141–3144.
Rakotomamonjy, A., Canu, S., 2005, http://asi.insarouen.fr/∼arakotom/toolbox/index.html.
Robertson, A. N., Farrar, C. R., Sohn, H., 2003, “Singularity Detection for Structural Health Monitoring Using Holder Exponents,”Mechanical Systems and Signal Processing, Vol. 17, No. 6, pp. 1163–1184.
Samanta, B., 2004, “Gear Fault Detection Using Artificial Neural Networks and Support Vector Machines with Genetic Algorithms,”Mechanical Systems and Signal Processing, Vol. 18, No. 3, pp. 625–644.
Shao, Y., Chang, C. -H., 2005, “Wavelet Transform to Hybrid Support Vector Machine and Hidden Markov Model for Speech Recognition,”IEEE ISCAS 2005, pp. 3833–3836.
Tahk, K. M., Shin, K. H., 2002, “A Study on the Fault Diagnosis of Roller-Shape Using Frequency Analysis of Tension Signals and Artificial Neural Networks Based Approach in a Web Transport System,”KSME International journal, Vol. 16, No. 12, pp. 1604–1612.
Vapnik, V. N., 1999, “An Overview of Statistical Learning Theory,”IEEE Transaction on Neural Networks, Vol. 10, No. 5, pp. 988–999.
Xu, Y., Ge, M., 2004, “Hidden Markov Model-Base Process Monitoring System,”Journal of Intelligent Manufacturing, Vol. 15, pp. 337–350.
Yang, B. S., Han, T., Hwang, W. W., 2005, “Fault Diagnosis of Rotating Machinery Based on Multiclass Support Vector Machines,”Journal of Mechanical Science and Technology, Vol. 19, No. 3, pp. 846–859.
Yuan, S. F., Chu, F. L., 2006, “Support Vector Machines-Based Fault Diagnosis for Turbo-Pump Rotor,”Mechanical Systems and Signal Processing, Vol. 20, No. 4, pp. 939–952.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Miao, Q., Huang, HZ. & Fan, X. A comparison study of support vector machines and hidden Markov models in machinery condition monitoring. J Mech Sci Technol 21, 607–615 (2007). https://doi.org/10.1007/BF03026965
Received:
Revised:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF03026965