Multi-class fault diagnosis based on support vector machines with sequenced binary tree archtecture

Zhendong yu Chongji/Journal of Vibration and Shock

Volumn 28, Issue 3, 2009, Pages 51-54

  Yuan, Sheng Fa ^a   Chu, Fu Lei ^b  

^a WUHAN UNIVERSITY OF TECHNOLOGY   (China)

^b TSINGHUA UNIVERSITY   (China)

Author keywords

Binary tree; Fault diagnosis; Multi class support vector machine; Support vector machines

Indexed keywords

BINARY TREE ARCHITECTURES; FAULT DIAGNOSIS; FAULT RECOGNITION; MULTI CLASS; MULTI-CLASS SUPPORT VECTOR MACHINE; NODAL POINTS; SAMPLE DISTRIBUTIONS;

BINARY TREES; CLASSIFIERS; GEARS; IMAGE RETRIEVAL; MULTILAYER NEURAL NETWORKS; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 64249129987     PISSN: 10003835     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. Widodo A, Yang B S. Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors[J]. Expert Systems with Applications, 2007, 33(1): 241-250.
2. Ravikumar B, Thukaram D, Khincha H P. Application of support vector machines for fault diagnosis in power transmission system[J]. Iet Generation Transmission and Distribution, 2008, 2(1): 119-130.
3. Abbasion S, Rafsanjani A, Farshidianfar A, et al. Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine[J]. Mechanical Systems and Signal Processing, 2007, 21(7): 2933-2945.
4. Acevedo F J, Maldonado S, Dominguez E, et al. Probabilistic support vector machines for multi-class alcohol identification[J]. Sensors and Sctuatiors B-Chemical, 2007, 122(1): 227-235.
5. Sungmoon C, Sang H O, Soo-Young L. Suppor vector machines with binary tree architecture for multi-class classification[J]. Neural Information Processing-Letters and Reviews, 2004, 2(3): 47-51.
6. Fumitake Takahashi, Shigego Abe. Decision-tree-based multi-class support vector machines, From; Http://frenchblue.scitec.kobe-u.ac.jp/abe/pdf/iconip02-takashi.pdf, 2002.
7. Vapnik V N. The nature of statistical learning theory. New York: Springer-Verlag, 1999.