Multiclass fault diagnosis in gears using support vector machine algorithms based on frequency domain data

Measurement: Journal of the International Measurement Confederation

Volumn 46, Issue 9, 2013, Pages 3469-3481

  Bansal, S ^a   Sahoo, S ^a   Tiwari, R ^a   Bordoloi, D J ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY GUWAHATI   (India)

Author keywords

Fault diagnostics; Frequency domain; Gear faults; Support vector machines; Vibration signature

Indexed keywords

EXTRAPOLATION; FAULT DETECTION; FREQUENCY DOMAIN ANALYSIS; INTERPOLATION; SUPPORT VECTOR MACHINES; VIBRATIONS (MECHANICAL);

FAULT DIAGNOSTICS; FREQUENCY DOMAINS; GEAR FAULTS; GENERALIZATION CAPABILITY; MACHINE LEARNING METHODS; MULTI-CLASS CLASSIFICATION; SUPPORT VECTOR MACHINE ALGORITHM; VIBRATION SIGNATURE;

CLASSIFICATION (OF INFORMATION);

EID: 84881172864     PISSN: 02632241     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.measurement.2013.05.015     Document Type: Article

References (14)

1. A. Tripathi, A. Garg, T. Chakraborty and R. Tiwari, 2010, Automation of the Health Monitoring of Induction Motors by Support Vector Machine Learning Algorithms, National Conference on Industrial Problems on Machines and Mechanisms, IPROMM-2010, pp. 139-145, MNIT Jaipur, India, December 17-18, 2010.
2. A. Widodo, and B.S. Yang Support vector machine in machine condition monitoring and fault diagnosis Mechanical Systems and Signal Processing 21 2007 2560 2574 (Pubitemid 46829756)
3. V. Vapnik Statistical Learning Theory 1998 John Wiley & Sons London
4. N. Cristianini, and J.S. Taylor An Introduction to Support Vector Machines and Other Kernel-based Learning Methods 2000 Cambridge University Press London
5. C.W. Hsu, C.C. Chang, and C.J. Lin A Practical Guide to Support Vector Classification 2008 Dept. of Comp. Sc., National Taiwan University Taiwan
6. B. Samanta Artificial neural networks and genetic algorithms for gear fault detection Mechanical Systems and Signal Processing 18 2004 1273 1282
7. B. Samanta Gear fault detection using artificial neural networks and support vector machines with genetic algorithms Mechanical Systems and Signal Processing 18 2004 625 644
8. D.M.J. Tax, A. Ypma, and R.P.W. Duin Pump Failure Determination using Support Vector Data Description, Lecture Notes in Computer Science 1999 Springer Berlin/Heidelberg 415-425
9. OSU-SVM: A Support Vector Machine Toolbox for MATLAB, Version 3.00, 2003. .
10. R. Tiwari, N. Tanwar, and S. Mishra Application of Support Vector Machine Techniques for Health Monitoring of Gears, National Conference on Condition Monitoring (NCCM-2009) 2009 NSTL Visakhapatnam, India December 4-5
11. B.E. Boser, I. Guyon, V. Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, ACM Press, 1992, pp. 144-152. (Pubitemid 23615470)
12. C. Cortes, and V. Vapnik Support-vector network Machine Learning 20 1995 273 297
13. B. Scholkopf, A. Smola, R.C. Williamson, and P.L. Bartlett New support vector algorithms Neural Computation 12 2000 1207 1245
14. Ryan. Rifkin, and Aldebaro. Klautau In defense of One-vs-All classification Journal of Machine Learning Research 5 2004 101 141