Feature extraction of rolling bearing fault signal based on local mean decomposition and Teager energy operator
Abstract
Purpose
This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method.
Design/methodology/approach
By combining local mean decomposition (LMD) with Teager energy operator, a new feature-extraction method of a rolling bearing fault signal was proposed, called the LMD–Teager transform method. The principles and steps of method are presented, and the physical meaning of the time–frequency power spectrum and marginal spectrum is discussed. On the basis of comparison with the fast Fourier transform method, a simulated non-stationary signal was processed to verify the effect of the new method. Meanwhile, an analysis was conducted by using the recorded vibration signals which include inner race, out race and bearing ball fault signal.
Findings
The results show that the proposed method is more suitable for the non-stationary fault signal because the LMD–Teager transform method breaks through the difficulty of the Fourier transform method that can process only the stationary signal. The new method can extract more useful information and can provide better analysis accuracy and resolution compared with the traditional Fourier method.
Originality/value
Combining the advantage of the local mean decomposition and the Teager energy operator, the LMD–Teager method suits the nature of the fault signal. A marginal spectrum obtained from the LMD–Teager method minimizes the estimation bias brought about by the non-stationarity of the fault signal. So, the LMD–Teager transform has better analysis accuracy and resolution than the traditional Fourier method, which provides a good alternative for fault diagnosis of the rolling bearing.
Keywords
Acknowledgements
The author wishes to acknowledge the assistance and support of all those who contributed to the effort to enhance and develop the described method. The author expresses appreciation for the financial support provided by the “2011” Hunan Province Cooperative Innovation Center for The Construction & Development of Dongting Lake Ecological Economic Zone. National Natural Science Foundation of China (Project No: 41304098), Key Research Fund of Hunan Provincial Education Department, PRC (Project No:16A146).
Citation
Cai, J. (2017), "Feature extraction of rolling bearing fault signal based on local mean decomposition and Teager energy operator", Industrial Lubrication and Tribology, Vol. 69 No. 6, pp. 872-880. https://doi.org/10.1108/ILT-12-2015-0200
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited