| Issue |
MATEC Web Conf.
Volume 327, 2020
2020 4th International Conference on Measurement Instrumentation and Electronics (ICMIE 2020)
|
|
|---|---|---|
| Article Number | 03003 | |
| Number of page(s) | 4 | |
| Section | Mechanical Design and Vehicle Engineering | |
| DOI | https://doi.org/10.1051/matecconf/202032703003 | |
| Published online | 06 November 2020 | |
Kurtosis Based Empirical Mode Decomposition for Rolling Bearing Fault Detection
1 School of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin, China
* Corresponding author: huili68@163.com
A bearing fault diagnosis approach based on spectral kurtosis and empirical mode decomposition (EMD) is proposed. EMD is a signal decomposition technique, which can adaptively separate a number of intrinsic mode functions (IMFs) from the vibration signal according to the architectural characteristics of the data. The spectral kurtosis parameter takes as signal impulsive indicator. Firstly, EMD is utilized to process the sampling vibration signal. And then spectral kurtosis is calculated to select the optimal intrinsic mode functions, so as to suppress the noise and highlight the transient impact feature. Finally, the envelope spectrum is computed and the fault characteristic is recognized. The experimental results show that the proposed approach can identify bearing defects effectively and provide a reliable method for gearbox fault monitoring and diagnosis.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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