Bearing fault diagnosis based on intrinsic time-scale decomposition and extreme learning machine

Fei Wang1, Wenjin Zhang2, Yu Ding3

School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China

Science and Technology on Reliability and Environmental Engineering Laboratory, Beijing, 100191, China

3Corresponding author

E-mail: 1wangnimao@126.com, 2zwjok@buaa.edu.cn, 3iconstantine@foxmail.com

Received 26 September 2017; accepted 3 October 2017

DOI https://doi.org/10.21595/vp.2017.19198

 

Abstract. Fault diagnosis for bearings is a focus and difficulty in diagnosis research area, so an intelligent diagnosis method using intrinsic time-scale decomposition(ITD) and extreme learning machine (ELM) is proposed in this paper. ITD is a relatively practical non-stationary signal decomposition method, which can decompose non-stationary signal into several components. Then, coefficient of kurtosis was extracted, which was acquired to reduce feature dimensions. Last, in order to reduce man-made interference and increase diagnostic accuracy, ELM was applied to identify and classify bearing states. The experimental result shown that above methods work well in classification and diagnosis for bearings state timely.

Keywords: fault diagnosis, ITD, ELM.

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Cite this article

Wang Fei, Zhang Wenjin, Ding Yu Bearing fault diagnosis based on intrinsic time‑scale decomposition and extreme learning machine. Vibroengineering PROCEDIA, Vol. 14, 2017, p. 97‑101.

 

JVE International Ltd. Vibroengineering PROCEDIA. Oct 2017, Vol. 14. ISSN 2345-0533