Adaptive Variational Mode Decomposition for Bearing Fault Detection ()
ABSTRACT
Rolling element bearings are commonly used in rotary
mechanical and electrical equipment. According to investigation, more than half
of rotating machinery defects are related to bearing faults. However, reliable
bearing fault detection still remains a challenging task, especially in
industrial applications. The objective of
this work is to propose an adaptive variational mode decomposition (AVMD) technique for non-stationary
signal analysis and bearing fault detection. The AVMD includes several
steps in processing: 1) Signal characteristics are analyzed to determine the
signal center frequency and the related parameters.
2) The ensemble-kurtosis index is suggested to decompose the target signal and select the most representative intrinsic mode functions (IMFs). 3) The envelope spectrum analysis is performed
using the selected IMFs to identify the characteristic features for
bearing fault detection. The effectiveness of the proposed AVMD technique is
examined by experimental tests under different bearing conditions, with the
comparison of other related bearing fault techniques.
Share and Cite:
Xing, X. , Zhang, M. and Wang, W. (2023) Adaptive Variational Mode Decomposition for Bearing Fault Detection.
Journal of Signal and Information Processing,
14, 9-24. doi:
10.4236/jsip.2023.142002.