Fault Diagnosis of Metallurgical Machinery Based on Spectral Kurtosis and GA-SVM

Yun Li; Yan Gao; Jun Guo; Xian Jun Yu

doi:10.4028/www.scientific.net/AMR.634-638.3958

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 634-638Fault Diagnosis of Metallurgical Machinery Based...

Fault Diagnosis of Metallurgical Machinery Based on Spectral Kurtosis and GA-SVM

Abstract:

This paper proposed a new method of rolling element bearing (REB) fault diagnosis for metallurgical machinery. Mainly it stresses on the combination of spectral kurtosis (SK) and supports vector machine (SVM), using genetic algorithm (GA) to optimize the parameters of support vector machine at the same time. Thus, this study aims to integrate SK, GA and SVM in order to develop an intelligent REB fault detector for metallurgical machineries. Simulation study indicates that this method can effectively detect the REB faults with a high accuracy.

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Periodical:

Advanced Materials Research (Volumes 634-638)

Pages:

3958-3961

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.634-638.3958

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Online since:

January 2013

Authors:

Yun Li, Yan Gao, Jun Guo, Xian Jun Yu

Keywords:

Bearing Fault, Diagnostics, Genetic Algorithm (GA), Spectral Kurtosis, Support Vector Machine (SVM)

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References

[1] Yongxiang Zhang, R.B. Randall, Rolling Element Bearing Fault Diagnosis Based on the Combination of Genetic Algorithm and Fast Kurtogram, Mechanical Systems and Signal Processing 23 (2009) 1509–1517.

DOI: 10.1016/j.ymssp.2009.02.003

Google Scholar

[2] Yu Guo, Ting-Wei Liu, Jing Na, Rong-Fong Fung, Envelope order tracking for fault detection in rolling element bearings, Journal of Sound and Vibration, In Press (2012)

DOI: 10.1016/j.jsv.2012.07.026

Google Scholar

[3] Jerome Antoni, The Spectral Kurtosis: A Useful Tool for Characterizing Non-stationary Signals, Mechanical Systems and Signal Processing 20 (2006) 282–307.

DOI: 10.1016/j.ymssp.2004.09.001

Google Scholar

[4] V.N. Vapnik, Statistical Learning Theory, New York, New York: John Wiley and Sons, 1998.

Google Scholar

[5] Yang Jie, Zheng Ning, Liu Dong, SVM Weighted Features and Model Parameters Optimization Based on Genetic Algorithm, Computer Simulation (2008), 25(9):113-118.In Chinese.

Google Scholar