Abstract
In order to identify incipient rolling bearing pitting fault characteristics, an autocorrelation based multi-structure elements difference morphological filter and empirical mode decomposition method of fault diagnosis is presented in this paper. Through the experiment of rolling bearing inner and outer ring pitting failure, the fault vibration frequency is extracted to verify the feasibility of this method. The superiority of this method is verified by comparing with the empirical mode decomposition method with autocorrelation based multi-structure element mixed morphological filter and without filter. DOI: http://dx.doi.org/10.5755/j01.mech.24.6.22471
Highlights
In 1998, Huang et al put forward an empirical mode decomposition (EMD) method [1], which was suitable for nonlinear and non-stationary signal analysis
Rai et al Encouraged a novel method for bearing performance degradation assessment (PDA) based on an amalgamation of empirical mode decomposition (EMD) and k-medoids clustering [10]
The method is applied to the analysis of the rolling bearing inner and outer ring pitting fault signal, which is compared with EMD fault diagnosis methods with autocorrelation based multi-structure element mixed morphological filter and without filter to verify the effectiveness and superiority of this method
Summary
In 1998, Huang et al put forward an empirical mode decomposition (EMD) method [1], which was suitable for nonlinear and non-stationary signal analysis. It was a major breakthrough in the methods of linear and steadystate spectral analysis based on Fourier transform. Liu et al applied the EMD method and Hilbert-Huang transform to the fault diagnosis of gear box [2]. The method is applied to the analysis of the rolling bearing inner and outer ring pitting fault signal, which is compared with EMD fault diagnosis methods with autocorrelation based multi-structure element mixed morphological filter and without filter to verify the effectiveness and superiority of this method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
