Fault Diagnosis for Rotator in Rotating Machinery Based on Support Vector Machine

Wei Niu

doi:10.4028/www.scientific.net/AMM.532.102

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 532Fault Diagnosis for Rotator in Rotating Machinery...

Fault Diagnosis for Rotator in Rotating Machinery Based on Support Vector Machine

Abstract:

As failure of rotator in rotating machinery has a certain concealment, fault diagnosis for rotator in rotating machinery based on support vector machine with particle swarm optimization algorithm is presented in the paper. And particle swarm optimization algorithm is applied to select the suitable parameters of support vector machine. In the study, we employ three PSO-SVM classifiers to recognize the four states of rotator in rotating machinery including normal state, rotor imbalance, rotor winding and rotor misalignment. More than 70 cases are used to testify the effectiveness of the PSO and SVM model compared with other classification models. The experimental results show that diagnostic precision for rotating machinery of PSO and SVM than that of SVM and BPNN.

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Periodical:

Applied Mechanics and Materials (Volume 532)

Pages:

102-105

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.532.102

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Online since:

February 2014

Authors:

Wei Niu*

Keywords:

Fault Diagnosis, Particle Swarm Optimization Algorithm, Rotating Machinery, Support Vector Machine (SVM)

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References

[1] Michael Q. Freehill, Guido Marra, Evaluation and treatment of failed rotator cuff repairs, Operative Techniques in Orthopaedics, 2003, vol. 13, no. 4, pp.269-276.

DOI: 10.1016/s1048-6666(03)00077-6

Google Scholar

[2] Shiwei Yu, Kejun Zhu, Fengqin Diao, A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction, Applied Mathematics and Computation, 2008, vol. 195, no. 1, pp.66-75.

DOI: 10.1016/j.amc.2007.04.088

Google Scholar

[3] Shinya Katagiri, Shigeo Abe, Incremental training of support vector machines using hyperspheres, Pattern Recognition Letters, 2006, vol. 27, no. 13, pp.495-507.

DOI: 10.1016/j.patrec.2006.02.016

Google Scholar

[4] P. S. Li, S. H. Xu. Support vector machine and kernel function characteristic analysis in pattern recognition, Computer Engineering and Design, 2005, vol. 26, no. 2, pp.302-304.

Google Scholar

[5] W.F. Abd-El-Wahed, A.A. Mousa, M.A. El-Shorbagy, Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems, Journal of Computational and Applied Mathematics, 2011, vol. 235, no. 5, pp.1446-1453.

DOI: 10.1016/j.cam.2010.08.030

Google Scholar

[6] Fan H Y. A Modification to Particle Swarm Optimization Algorithm. Engineering Computations, 2002, vol. 19, pp.970-989.

DOI: 10.1108/02644400210450378

Google Scholar