The Support Vector Machine Fault Diagnosis Algorithm Based on Cloud Computing

Chang Hong Zhang; Si Jia Cheng; Shu Hao Cao

doi:10.4028/www.scientific.net/AMM.687-691.761

Paper Titles

Large-Scale Complex Aircraft Part Digital Inspection Technology Based on 3D Model
p.743

The Remote Monitoring System of Highway Slope Safety
p.747

Redundancy Design in the Digital Instrumentation and Control System Design of Nuclear Power Plant
p.751

Remote Control and Communication System for Rescue and Detection Robot with Intrinsic Safety in Coal Mine
p.755

The Support Vector Machine Fault Diagnosis Algorithm Based on Cloud Computing
p.761

Application of Heart Rate Monitoring in Badminton Training
p.766

Methods for Monitoring the Temperature of GIS Bus Conductor
p.770

An Array Design Method and Error Analysis for Terminal Trajectory Measurement
p.774

Optimal Design of the Wireless Sensor Network on Environmental Monitoring
p.779

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691The Support Vector Machine Fault Diagnosis...

The Support Vector Machine Fault Diagnosis Algorithm Based on Cloud Computing

Abstract:

The paper puts forward the way to solve the problem of SVM training on the large scale firstly, Then perform the experiment to verify the feasibility of scheme. In the last section, SVM fault diagnosis method based on the Mapreduce is put forward.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

761-765

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.761

Citation:

Cite this paper

Online since:

November 2014

Authors:

Chang Hong Zhang, Si Jia Cheng, Shu Hao Cao

Keywords:

Cloud Computing, Fault Diagnosis, Incremental Algorithm, Least Squares Support Vector Machines (Ls-SVM), Sparsity

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J.H. Xu, X.G. Zhang and Y.D. Li. The new development of support vector machine. Control and decision making, 2004, 19(5): 481-484.

Google Scholar

[2] S.X. Du and T.J. Wu. Support vector machine method for regression estimation. Journal of system simulation, 2004, 15(11): 1580-1585.

Google Scholar

[3] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293-300.

Google Scholar

[4] M. Miller. Cloud computing. Beijing: China Machine Press, (2009).

Google Scholar

[5] Suykens J A K, De Brabanter J, Gesel T V. Least squares support vector machines. World Scientific, (2002).

DOI: 10.1142/5089

Google Scholar

[6] Suykens J A K, De Brabanter J, Lucas L, et al. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 2002, 48 (1): 85-105.

DOI: 10.1016/s0925-2312(01)00644-0

Google Scholar

[7] Tsallis C. Possible generalization of Boltzmann-Gibbs statistics. Journal of statistical physics, 1988, 52(1-2): 479-487.

DOI: 10.1007/bf01016429

Google Scholar

[8] Espinoza M, Suykens J A K, Moor B D. Fixed-size least squares support vector machines: A large scale application in electrical load forecasting. Computational Management Science, 2006, 3 (2): 113-129.

DOI: 10.1007/s10287-005-0003-7

Google Scholar