Data fusion for fault diagnosis using multi-class Support Vector Machines

Journal of Zhejiang University: Science

Volumn 6 A, Issue 10, 2005, Pages 1030-1039

  Hu, Zhong Hui ^a   Cai, Yun Ze ^a   Li, Yuan Gui ^a   Xu, Xiao Ming ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Data fusion; Diesel engine; Fault diagnosis; Multi class classification; Multi class support vector machine

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; DIESEL ENGINES; FAILURE ANALYSIS; OPTIMIZATION; ROBUSTNESS (CONTROL SYSTEMS); VECTORS;

DATA FUSION STRATEGY; FAULT DIAGNOSIS; MULTICLASS CLASSIFICATION; MULTICLASS SUPPORT VECTOR MACHINE;

PRODUCTION ENGINEERING;

EID: 27944467534     PISSN: 10093095     EISSN: None     Source Type: Journal    
DOI: 10.1631/jzus.2005.A1030     Document Type: Article

References (19)

1. Benediktsson, J.A., Sveinsson, J.R., Ersoy, O.K., Swain, P.H., 1997. Parallel consensual neural networks. IEEE Transactions on Neural Networks, 8(1): 54-64.
2. Burges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2): 121-167.
3. Byington, C.S., Garga, A.K., 2001. Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems. In: Hall, D.L., Llinas, J.(Eds.), Handbook of Multisensor Data Fusion. CRC Press, Boca Raton, FL, p.1-32.
4. Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press, Cambridge.
5. Duda, R.O., Hart, P.E., Stork, D.G., 2001. Pattern Classification (2nd Edition). John Wiley and Sons, Inc., New York, p.483.
6. Friedman, J., 1996. Another Approach to Polychotomous Classification. Technical Report, Department of Statistics, Stanford University, Stanford, CA.
7. Grox, X.E., 1997. NDT Data Fusion. John Wiley and Sons, Inc., New York, p.1-42.
8. Hajiaghajani, M., Toliyat, H.A., Panahi, I.M.S., 2004. Advanced fault diagnosis of a DC motor. IEEE Transactions on Energy Conversion, 19(1): 60-65.
9. Hall, D.L., Llinas, J., 1997. An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1): 6-23.
10. Hsu, C.W., Lin, C.J., 2002. A comparison of methods for multi-class Support Vector Machines. IEEE Transactions on Neural Networks, 13(2): 415-425.
11. Liggins, M.E. II, Chong, C.Y., Kadar, I., Alford, M.G., Vannicola, V., Thomopoulos, S., 1997. Distributed Fusion architectures and algorithms for target tracking. Proceedings of the IEEE, 85(1): 95-107.
12. Pan, H., Liang, Z.P., Anastasio, T.J., Huang, T.S., 1998. A Hybrid NN-Bayesian Architecture for Information Fusion. Image Processing, ICIP 98, 1: 368-371.
13. Papastavrou, J.D., Athans, M., 1992. On optimal distributed decision architectures in a hypothesis testing environment. IEEE Transactions on Automatic Control, 37(8): 1154-1169.
14. Platt, J.C., Cristianini, N., Shawe-Taylor, J., 2000. Large Margin DAG's for Multiclass Classification. In: Solla, S.A., Leen, T.K., Müller, K.R. (Eds.), Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 12: 547-553.
15. Shen, L., Tay, F.E.H., Qu, L., Shen, Y., 2000. Fault diagnosis using rough sets theory. Computers in Industry, 43: 61-72.
16. Tay, F.E.H., Shen, L., 2003. Fault diagnosis based on rough set theory. Engineering Application of Artificial Intelligence, 16: 39-43.
17. Thomopoulos, S.C.A., Viswanathan, R., Bougoulias, D.K., 1989. Optimal distributed decision fusion. IEEE Transactions on Aerospace and Electronic Systems, 25(5): 761-765.
18. Vapnik, V.N., 1998. Statistical Learning Theory. Wiley, New York.
19. Yan, W., Shao, H., Wang, X., 2003. Parallel Decision Models Based on Support Vector Machines and Their Application to Distributed Fault Diagnosis. Proceedings of the American Control Conference, 2: 1770-1775.