Fault Diagnosis for Motor Rotor Based on KPCA-SVM

Ping Li; Xue Jun Li; Ling Li Jiang; Da Lian Yang

doi:10.4028/www.scientific.net/AMM.143-144.680

Paper Titles

Chaos Character of Deviation Signal and Prediction of Tail Deviation in Strip's Rolling
p.658

AE Fault Diagnosis of Rolling Bearing Based on Base
p.664

Harmonic Response Analysis of Rotor-Rolling Bearing-Base System for Fault Diagnosis of Rotating Machinery Based on Base
p.669

Research on the Imbalance-Crack Coupling Fault Diagnosis Based on Wavelet Packet and Energy Spectrum Analysis
p.675

Fault Diagnosis for Motor Rotor Based on KPCA-SVM
p.680

A Kind of Design of Satellite Simulation System Based on HLA
p.685

Denoising of the Base Vibration Signal Based on EEMD
p.689

Study on High Anti-Vibration Machine Bed
p.694

Study on the Conical Spiral Tube Bundle Heat Exchangers with Pulsation Flow Generator Structure
p.698

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 143-144Fault Diagnosis for Motor Rotor Based on KPCA-SVM

Fault Diagnosis for Motor Rotor Based on KPCA-SVM

Abstract:

Aimed at the nonlinear properties of motor rotor vibration signal,a fault diagnosis method based on kernel principal component analysis (KPCA) and support vector machines (SVM) was proposed. Initial feature vectors of motor vibration signal were mapped into higher-dimensional space with kernel function. Then the PCA method was used to analyze the data in the high dimensional space to extract the nonlinear features which is used as training sample of SVM fault classifier. Then the rotor fault is identified using the trained classifier. The classification effect of KPCA-SVM is compared with PCA-SVM and SVM. The result shows that the method based on KPCA-SVM can identify motor rotor fault efficiently and fulfill fault classification accurately.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 143-144)

Pages:

680-684

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.143-144.680

Citation:

Cite this paper

Online since:

December 2011

Authors:

Ping Li, Xue Jun Li, Ling Li Jiang, Da Lian Yang

Keywords:

Fault Diagnosis, KPCA, Motor Rotor, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] H. Zh. Ma: Motor Condition Monitoring and Fault Diagnosis (China Machine Press, Beijing 2008).

Google Scholar

[2] Zh.N. Li, X.Y. Wang, Q.Y. Fu, X.G. Zhang: Noise and Vibration Control Vol. 5 (2008), pp.77-79.

Google Scholar

[3] Sch lkopf B, Smola A, Müller K R: Neural Computation Vol. 10-5(1998), pp.1299-1319.

Google Scholar

[4] Sch lkopf B, Smola A, Müller K R: Kernel Principal Component Analysis (1999), pp.327-352.

Google Scholar

[5] Y. Xu, D. Zhang, F.X. Song, et al: Neurocomputing Vol. 70 (2007), pp.1056-1061.

Google Scholar

[6] W.H. Li, G.L. Liao, T.L. Shi: Chinese Journal of Mechanical Engineering Vol. 39-8(2003), pp.65-70.

Google Scholar

[7] Cristianini N, Shawe-Taylor J: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (Cambridge University Press, Cambridge 2000).

DOI: 10.1017/cbo9780511801389

Google Scholar

[8] L.L. Jiang, Y.L. Liu, X.J. Li, An.H. Chen: Journal of Central South University Vol. 41-6(2010), pp.2184-2188.

Google Scholar

[9] Hsu C, Lin C J: IEEE Transactions on Neural Networks Vol. 13-2(2002), pp.415-425.

Google Scholar