Abstract
Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering However, their applications in fault diagnosis of rotating machinery are rather limited Most of the published papers focus on some special fault diagnoses This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator
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
Bishop C M, 1995,Neural Networks for Pattern Recognition, Oxford Clarendon Press
Bottou, L, Cortes, C, Denker, J, Drucker, H, Guyon, I, Jackel, L, LeCun, Y, Muller, U, Sackinger, E., Simard, P and Vapnik, V, 1994, “Comparison of Classifier Methods A Case Study in Handwriting Digit Recognition,”Proc. International Conference on Pattern Recognition, pp 77-87
Burges, C J C, 1998, “A Tutorial on Support Vector Machines for Pattern Recognition,”Data Mining and Knowledge Discovery, Vol 2, No 2, pp 955–974
Capenter, G A and Grossberg, S., 1988, “The ART of Adaptive Pattern Recognition by a Selforganizing Neural Network,”IEEE Trans. on Computers, Vol 21, No 3, pp 77–88
Cover, T M, 1965, “Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition,”IEEE Trans on Electronic Computers, Vol 14, pp 326–334
Daubechres, I, 1992,Ten Lectures on Wavelets, SIAM, Pennsylvania
Friedman, J, 2003,Another Approach to Polychotomous Classification, Department of Statistics, Stanford Univ., CA http //www-stst.stanford edu/report/friedman/poly ps Z
Hermes, L and Buhmann, J M, 2000, “Feature Selection for Support Vector Machines,”Proc. 15th International Conference on Pattern Recognition, pp 712-715.
Hsu, C W and Lin, C J., 2002, “A Comparison of Methods for Multiclass Support Vector Machines,”IEEE Trans, on Neural Networks, Vol 13, No 2, pp 415–425
Jack, L B and Nandi, A K., 2002, “Fault Detection Using Support Vector Machines and Artificial Neural Networks, Augmented by Genetic Algorithms,”Mechanical Systems and Signal Processing, Vol 16, No 2-3, pp 373–390
Kangas, J and Kohonen, T, 1996, “Developments and Applications of the Self-organizing Map and Related Algorithms,”Mathematics and Computers in Simulation, Vol.41, pp. 3–12.
Keerthi, S. S. and Shevade, S K., 2002,SOM Algorithm for Least Squares SVM Formulations, Technical Report CD-02-8 http //guppy mpe nus edu sg/-mpessk
Knerr, S, Personnaz, L and Dreyfus, G, 1990, “Single-layer Learning Revisited A Stepwise Procedure for Building and Training a Neural Network,” inNeurocomputing: Algorithms, Achitectures and Applications, J Fogelman, Ed Springer-Verlag, New York
Kohonen, T, 1995,Self — Organizing Maps, Springer—Verlag, New York
Kreβel, U, 1999, “Pairwise Classification and Support Vector Machines,” inAdvances in Kernel Methods-Support Vector Learning, B Scholkopf, C. J. C. Burges, A J Smola, Eds MIT Press, Cambridge, pp 255–268
Misiti, M, Misiti, Y., Oppenheim, G and Poggi, J M, 1996,Wavelet Toolbox for Use with MATLAB, The Math Works Inc
Muller, K R, Mika, S, Ratsch, G, Tsuda, K and Scholkopf, B., 2001, “An Introduction to Kernel-based Learning Algorithm,”IEEE Trans. on Neural Network, Vol 12, No. 2, pp 181–201
Ob, J H, Kint, C G and Cho, Y M, 2004, “Diagnostics and Prognostics Based on Adaptive Time-Frequency Feature Discrimination,”KSME International Journal, Vol 18, No 9, pp 1537–1548
Osuna, E, Freund, R and Girosi, F, 1997, “Training Support Vector Machines An Applications to Face Detection,”Proc CVPR, pp 1-6
Platt, J C, 1998,Sequential Minimal Optimization A Fast Algorithm for Traimng Support Vector Machines, Technical Report 98-14, Microsoft Research, Redmond, Washington http//www.research.microsoft.com/-Jplatt/smo.html
Platt, J C, Cristianini, N and Shawe-Talyor, J, 2000, “Large Margin DAG’s for Multiclass Classification,”Advances in Neural Information Processing Systems, Vol 12, pp 547–553
Raudys, S J and Jain, A K, 1991, “Small Sample Size Effects in Statistical Pattern Recognition Recommendations for Practitioners,”IEEE Trans on Pattern Analysis and Machine Intelligence, Vol 13, No 3, pp 252–264
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
Scholkopf, B, 1997,Support Vector Learning, Oldenbourg-Verlag, Germany
Schwenker, F, 2000, “Hierarchical Support Vector Machines for Multi-class Pattern Recognition,”Proc 4th International Conference on Knowledge-Based Intelligent Engineering Systems &Allied Technologies, pp 561-565
Smola, A J, Scholkopf, B and Muller, K R, 1998, “The Connection between Regulanzation Operators and Support Vector Kernels,”Neural Networks, Vol 11, pp 637–649
Smola, A J and Scholkopf, B, 1998,A Tutorial on Support Vector Regression, Technical Report NC2-TR-1998-030 http//www.neurocolt.com
Strauss, D. J and Steidel, G, 2002, “Hybrid Wavelet-Support Vector Classification of Wave-forms,”Journal of Computational and Applied Mathematics, Vol 148, pp 375–400
Sundararajan, N, Saratchandran, P and Wel, L Y, 1999,Radial Basis Function Neural Networks with Sequential Learning, World Scientific, Singapore
Suykens, J A K, Van Gestel, T, De Brabanter, J, De Moor, B and Vandewalle, J, 2002,Least Squares Support Vector Machines, World Scientific, Singapore
Vapnik, V N, 1982,Estimation of Dependences Based on Empirical Data, Springer-Ver-lag
Vapnik, V N, 1992,Principles of Risk Minimization for Learning Theory, pp 831–838 in J E Moody et al (Eds)Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, San Mateo, CA
Vapmk, V N, 1999,The Nature of Statistical Learning Theory, Springer, New York
Yang, B S, Lim, D S, Sco, S Y and Kim, M H, 2000a, “Defect Diagnostics of Rotating Machinery using SOFM and LVQ,”Proc. 7th International Congress on Sound and Vibration, pp 567-574
Yang, B S, Lim, D S and An, J L, 2000b, “Vibration Diagnostic System of Rotating Machinery using Artificial Neural Network and Wavelet Transform,”Proc. 13th International Congress on COMADEM, pp 923-932
Yang, B S, Kim, K and Rao, Raj B K N, 2002, “Condition Classification of Reciprocating Compressors using RBF Neural Network,”International Journal of COMADEM, Vol 5, No 4, pp 12–20
Yang, B S, Han, T and An, J L, 2004a, “ART-Kohonen Neural Network foi Fault Diagnosis of Rotating Machinery,”Mechanical Systems and Signal Processing, Vol 18, No 3, pp 645–657
Yang, B S, Han, T, An, J L, Kim, H C and Ahn, B H, 2004b, “A Condition Classification System for Reciprocating Compressois,”International Journal of Structural Health Monitoring, Vol 3, No 3, pp 277–284
Yang, B S, Hwang, W W, Kim, D J and Tan, A, 2005, “Condition Classification of Small Reciprocating Compressor for Refrigerators using Artificial Neural Networks and Support Vector Machines,”Mechanical Systems and Signal Processing, Vol 19, No 2, pp 371–390
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, BS., Han, T. & Hwang, WW. Fault diagnosis of rotating machinery based on multi-class support vector machines. J Mech Sci Technol 19, 846–859 (2005). https://doi.org/10.1007/BF02916133
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF02916133