Li, J., Yao, X., Wang, H. and Zhang, J. (2019) Periodic Impulses Extraction Based on Improved Adaptive VMD and Sparse Code Shrinkage Denoising and Its Application in Rotating Machinery Fault Diagnosis. Mechanical Systems and Signal Processing, 126, 568-589.
https://doi.org/10.1016/j.ymssp.2019.02.056

has been cited by the following article:

• TITLE: Adaptive Variational Mode Decomposition for Bearing Fault Detection

  AUTHORS: Xing Xing, Ming Zhang, Wilson Wang

  KEYWORDS: Bearing Fault Detection, Vibration Signal Analysis, Intrinsic Mode Functions, Variational Mode Decomposition

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.14 No.2, May 31, 2023

  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.